<![CDATA[Coding with Intelligence]]>https://codingwithintelligence.comhttps://substackcdn.com/image/fetch/$s_!OscA!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd83dbc94-e610-434a-80c7-f89ca4a78b1a_688x688.pngCoding with Intelligencehttps://codingwithintelligence.comSubstackSun, 05 Apr 2026 18:47:53 GMT<![CDATA[An update from Coding with Intelligence]]>https://codingwithintelligence.com/p/an-update-from-coding-with-intelligencehttps://codingwithintelligence.com/p/an-update-from-coding-with-intelligenceTue, 25 Nov 2025 14:25:24 GMTHi all, things have been crazy busy at Groq, with some very exciting releases coming up. As a result I haven’t been able to dedicate time to documenting what is happening in AI as much as I would have liked.

To integrate things in a way that feels sustainable with the work I’m doing I've decided to go X-only for the time being wrt updates on what is happening in AI.

In this edition I will highlight a number of recent posts from my X account that I think capture interesting developments of the past month. Of course you can just follow me on X to keep getting updates more frequently. In the future I might return to posting on Substack again, in the interim, I will occasionally highlight some of the most relevant posts I've seen/retweeted/written on X.

]]><![CDATA[New frontier? Continuous learning and RL environment scaling]]>https://codingwithintelligence.com/p/new-frontier-continuous-learninghttps://codingwithintelligence.com/p/new-frontier-continuous-learningThu, 04 Sep 2025 13:28:51 GMT

📰 News

  • Trust me bro, just one more RL scale up, this one is the real one with the good envs

    Chief scientist Ryan Greenblatt at Redwood Research saying the quiet part out loud. There's been a lot of attention for RL environments as a way to improve model's capabilities. The basic idea is to create realistic simulation environments in which language models run as agents (capable of using tools to interact with said environment) to improve the model's capability (by rewarding behaviors/paths that lead to a good result in the environment, like passing unit tests/reaching an objective).

    Ryan rightfully questions the narrative that we're close to a very large acceleration due to AI labs scaling up on these kinds of environments. His key argument is that he expects to a decent extent these kind of environments (e.g. basic coding environments with access to a terminal/compiler/unit test running) have already been used by top labs (e.g. at the Grok 4 launch scaled up RL was discussed) and that verification of non coding tasks could remain a bottleneck for creating useful and diverse RL environments.

    I especially appreciate the Q&A section where he entertains the strongest counterpoints people might have. Excellent format to defend his points. Overall though, he agrees that there's still a lot of potential in scaling up the number/diversity/quality of RL environments. What I appreciate in particular are the points around there being evidence for better verification techniques for non-trivially verifiable domains as likely evidenced by IMO gold medal (verifying a written math proof is not trivial, especially if not using Lean-like-solvers during inference). And the insight that we might be on an acceleration loop of RL environment creation due to SWE agents accelerating coding so much (as evidenced by Cursor/Codex/SWE-bench results) and that work being (potentially) largely automatable with human scientist/engineer oversight.

  • OpenAI puts the "Open" back in OpenAI with the launch of gpt-oss 20B & 120B models

    Awesome open source model drop by OpenAI, these MoE models with high expert count (128 and 32 respectively) perform remarkably well. Especially when combined with function calling.

    We're hosting them at Groq and I've build a demo of these models powering an AI chat that can interface directly with a spreadsheet (all in your browser!): https://autosheet.groqlabs.com/ (that project itself is also open source, hack the source!)

  • MCPMark: a comprehensive evaluation suite for evaluating the agentic ability of frontier models

    A modern agentic eval that measures the performance of various models on e2e tasks involving MCP servers focused on Filesystems, Notion, Playwright, GitHub, Postgres. Interesting lead for GPT-5, although it's on par with Claude 4.1 Opus on the Notion tasks.

  • Gemini ships SOTA image editing model

    Gemini 2.5 Flash Image aka nano-banana has been floating in the ether for a while now with early access on Yupp.ai and LMArena. The model is incredibly powerful for stable image editing where only targeted modifications are applied. It beats both OpenAI's gpt-image-1 and Gemini's previous model by a wide margin as evidence by the Arena scoring gap. It is so good that on Reddit people are posting comparisons of old Photoshop paid work they've done and how nano-banana zero-shots these tasks, often with higher quality (like getting reflections right). If you were looking for specific and real examples of work displacement than look no further. Incredible release putting the Gemini chat app in the lead of image editing/generation capabilities.

  • Grok enters AI coding with Grok Code Fast 1

    The model is quite fast, but quality seems to be close to/slightly worse than Claude Sonnet 4 in practice. If they can keep the speed and boost the quality to GPT-5 level they would have a real killer combo on their hands. They subsidized the launch heavily by giving away a lot of tokens to the key coding agent (GitHub Copilot, Cline, opencode, Cursor, Kilo Code, Roo Code, Windsurf) and OpenRouter. Leading to increased adoption and them leading on OpenRouter coding token consumption (momentum seems to have kept up even as the free period has ended). I suspect the speed will be copied by others as the qualitative effect of "staying in flow" is really valuable while coding. <groq-shill>If anyone wants to achieve that using novel chip architecture (LPUs) hit me up! </groq-shill>

  • GPT-5: Key characteristics, pricing and model card

    GPT-5 has been out now for close to a month, and I think overall the routing mode has not been received very well (in ChatGPT the Auto mode often selects the fast models on questions that it should really use the thinking model for and gets it wrong as a consequence). The GPT-5 Thinking model is quite good but takes a long time. They've seemed to have pushed themselves out of the attractive "high quality instant but non-reasoning" model market for the moment. Direct non-thinking responses from Opus 4.1 feel in a league of their own compared to GPT-5 Instant as an example.

    This article by Simon Willison does a great job capturing all the nuance of the release.

    I'd add that in my anecdotal use of GPT-5 inside Cursor the model performs really really well, although for trickier problems I tend to switch to Opus 4.1, which is way to expensive to use for everything (at about 10x the cost of GPT-5). I suspect GPT-5 is good in Cursor because one of the areas it shines in is steerability. And I think Cursor adds a lot of sensible context in their (tool) prompts and I like being quite specific about how the model implements things as I'm generally not vibe coding but just doing "assisted software engineering".

  • Gemini teases Genie 3: a high definition world model simulator that can be manipulated

    The example of the paint brush painting a wall with persistence (even when looking away) is quite compelling. No playable demo unfortunately. Will be interesting what the value of world model simulators like this will be moving forward. Will they be the basis for embodied simulation RL training or more of a standalone product experience like promptable game engines for games/interactive experiences?

  • Qwen-Image & Qwen-Image-Edit

    Strong image editing capabilities, best-in-class open source model alternative to nano-banana/Gemini Flash Image 2.5 with capabilities similar to Flux Kontext (as one Reddit user puts it "I'd say Qwen Image editing is slightly superior to Kontext in prompt following when editing image")

  • Gemini 2.5 Pro Deep Think ships in Gemini app

    Ultra subs only. No API access. But pretty wild such strong intelligence available pretty much self-serve.

  • Fast Reasoning on GPT-OSS with Speculative Decoding and Arctic Inference

    Interesting work from Snowflake accelerating open source inference by using the hidden state of the model to predict more tokens ahead as a form of speculative decoding.

  • LongCat-Flash-Chat: a 560BA18.6B∼31.3B MoE

    Another foundational model release from China that pushes the frontier with a novel dynamic activation parameter count that is context window specific. It outperforms DeepSeek V3.1 on certain benchmark truly pulling its weight in the frontier bucket and reaching parity with Claude 4 Sonnet on many coding tasks. An interesting artifact to study and good model to run. Perhaps more importantly, indicating fierce open source competition in China leading potentially to more open source model development acceleration.

  • GLM-4.5V: a very strong vision language model by Zhipu AI

    Based on GLM-4.5 Air (108B) significantly outperforming Gemma 3 27B.

  • Prime Intellect announces Environments Hub

    A cool project to facilitate a push for open source RL environment development. See the "Trust me bro, just one more RL scale up" post for more context on the need for these kinds of RL environments. Will be interesting to see how many environments get created and what experimental RL training runs show in terms of gains from these environments on downstream tasks. At the time of writing about 125 environments seem to have been created.

📦 Repos

  • Cartridges: Storing long contexts in tiny caches with self-study

    An exploration of continuous learning by the Stanford Hazy Research lab to train KV caches labeled "cartridges", they train a KV cache per corpora (a collection of documents) and use the trained KV cache during inference as an alternative to loading the entire set of documents into the context window.

    Radical idea with significant performance implications: using 38.6× less memory and enabling 26.4× higher throughput. It's still more costly than In-Context Learning (ICL, e.g. just stuffing in the context window) in terms of training time (30m for a Cartridge on a Llama 3 8B) v.s. pre-fill time, but there are ideas for speeding this up.

  • OpenPipe: e2e example of RL training a Deep Research agent on open source model for email search

    Noteworthy, OpenPipe was just acquired by CoreWeave. Likely to assist with training open source models for specific use cases using SFT/RL.

    The implementation is based on the open source ART learning framework by OpenPipe which itself is based on torchtune.

  • Open-dLLM: Open Diffusion Large Language Models

    No other diffusion LLM project had open sourced both data and training code, only projects with inference, evaluation, and weights were available so far. This project changes that with total openness on all of those dimensions. Helpful to study diffusion-based LLMs e2e! Performance on coding tasks like HumanEval lags quite a bit behind other open weight models like Dream 7B (20.8 vs 56.7) so there might be some suboptimal choices in architecture, training, data.

  • OpenVision 2: simplified pretraining and stronger vision encoder performance

    Continued impressive work on vision encoders by the team at UCSC (and folks from Apple/Berkeley). Especially useful due to the full transparency on method, training code and data. OpenVision 2 boasts 2x reduction in training time and memory use while achieving higher quality than the original OpenVision encoders.

📄 Papers

  • Jointly Reinforcing Diversity and Quality in Language Model Generations

    A new paper from the Meta FAIR team says the usual push for accuracy and helpfulness tends to squeeze out response diversity, which hurts creativity. They show you can optimize for both diversity and quality at the same time, with strong results on verifiable math and creative writing. The good news: there seems to be no inherent trade-off. Potentially less AI slop in the future!

  • VerlTool: a toolkit for tool-integrated reasoning training

    They released a paper show performance gains from using VerlTool versus other approaches of training LLM agents to use tools during reasoning. This is like the GPT-5 Thinking/o3 mode in ChatGPT that can use image manipulation and web search while it's rolling out its Chain-of-Thought reasoning. Their framework VerlTool allows you to train this capability into an existing model using RL training algorithms like GRPO and DAPO.

📚 Resources

  • Grok 2 model was open sourced

    Interesting artifact to study, but probably not very useful as other better models have been released. https://huggingface.co/xai-org/grok-2/discussions/24 some details on the architecture: 270B Total, 115B Activated MoE with 8 experts. Based on the Grok 2 launch blog post it reaches the level of Claude Sonnet 3.5 on tasks like GPQA and HumanEval (a little below).

  • The Hidden Drivers of HRM's Performance on ARC-AGI

    A breakdown of the HRM's model performance on ARC-AGI-1 (41%) with a 27M model (small!).

  • Automatically Jailbreaking Frontier Language Models with Investigator Agents

    "We train investigator agents using reinforcement learning to generate natural language jailbreaks for 48 high-risk tasks involving CBRN materials, explosives, and illegal drugs. Our results show success against models including GPT-5-main (78%), Claude Sonnet 4 (92%), and Gemini 2.5 Pro (90%). We find that small open-weight investigator models can successfully attack frontier target models, demonstrating an approach to cost-effective red-teaming." couldn't have said it better. Interesting find + deep dive on how jailbreaking isn't solved, even at the frontier.

  • [Video playlist] GPU MODE meetup talk recordings

    GPU MODE usually brings out very competent operators and this time MoE, long context, quantization are discussed (and some more).

  • Slides of Denny Zhou Gemini reasoning lead at Google DeepMind

    Shows reasoning was built on principles of Self-Consistency and that more work on verification is needed to make progress on real-world tasks.

  • Browser Use Agent benchmark by Princeton's Holistic Agent Leaderboard on OpenAI/Anthropic latest models

    They find that there's a large gap between pass@1 and pass@any which indicates there might be a way to get to much higher pass@1 through further optimization (i.e. since pass@any shows there is some way for the current agents to perform the task in the browser, there might be a way to elicit the right behaviors every time, rather than a need for a fundamental capability unlock to achieve these browser tasks). The best model+scaffold combination scores 42.3% on the Online Mind2Web benchmark while pass@any is 88.3%.

  • Cursor: 1.5x Faster MoE Training with Custom MXFP8 Kernels

    Cursor flexing their model training muscle. With Cursor's phenomenal adoption success it's likely they'll do more & more work to compete with frontier labs on coding (adjacent) models. Interesting deep dive on how to get MXFP8 training to be correct & efficient on Blackwell B200s.

  • Inside vLLM: Anatomy of a High-Throughput LLM Inference System

    Aleksa Gordić has a knack for teaching, this blog post effortlessly runs you through the key components of vLLM, from advanced features like disaggregated prefill/decoding to the basics of single to multi-GPU deployment. Great visualizations too!

  • ast-grep-mcp

    Equipping SWE agents with specialized MCP tools to make it operate more intelligently increases the utility and efficiency of these coding agents. ast-grep-cmp gives agents like Cursor and Claude Code the ability to perform structural code search (which as a refresher, is a tool that uses a syntax where you write code patterns with $METAVAR placeholders (like $A, $B) to match and capture AST nodes, allowing you to search for structural code patterns rather than just text).

  • The Illustrated GPT-OSS

    A nice blog post detailing the gpt-oss models released by OpenAI. From model architecture to the new chat template system (harmony).

  • Global Software Optimization (GSO) SWE leaderboard

    A benchmark for very complex SWE tasks, best frontier models score only 8.9% (OpenAI's o3, scores above GPT-5 Thinking high reasoning effort). What I appreciate in particular is the sample descriptions given showing the root of the task and how models fail in solving them. Very helpful new benchmark as labs are closer to saturating benchmarks like SWE-bench Verified.

  • S3GD Optimizer Algorithm

    >Smoothed SignSGD is more computationally and memory efficient relative to Adam variants, pairing perfectly with its sparsity to make it a compelling candidate for both SFT and RFT

    New optimizers being shared, will be interesting to follow the other releases of the WhyPhy team.

Want more? Follow me on X! @ricklamers

]]><![CDATA[Qwen team drops 3 (!) SOTA models in one week]]>https://codingwithintelligence.com/p/qwen-team-drops-3-sota-models-inhttps://codingwithintelligence.com/p/qwen-team-drops-3-sota-models-inFri, 25 Jul 2025 15:18:14 GMT
Qwen3-Coder benchmark scores

📰 News

  • Qwen releases Qwen3-Coder + a fork of gemini-cli for a terminal coding agent

    It is a large MoE model (Qwen3-Coder-480B-A35B-Instruct) with almost double the active parameters compared to the updated Qwen3 235B general reasoning/non-reasoning models. It supports a 256k context window natively with YaRN based extension to 1M (available on the Alibaba-hosted endpoint). The focus seems to be on the ability of the model to be strong at agentic coding tasks as required in the context of Claude Code-esque tools.

    Their fork, called qwen-code, of the open source gemini-cli (similar to Claude Code) was done to optimize the prompts for the Qwen3-Coder model. Open Source models tend to benefit an outsized amount from prompt tweaks/iteration for maximally stable performance.

    The headline performance number is the SWE-bench Verified subset and it reaches 67% vs 68% (Claude Sonnet 4) vs 48.6 (GPT-4.1), which is impressive to say the least. On terminal bench it reaches 37.5% (vs 35.5% for Claude Sonnet 4), which is crucial in coding tasks that involve running e.g. npm/git/script commands. For the full benchmark figures see the blog post.

  • Qwen releases Qwen3-235B-A22B updates: dedicated thinking and non-thinking models with close to SOTA performance

    It outperforms Gemini 2.5 Pro on LiveCodeBench v6 (25.02-25.05) with 74.1% vs 72.5% while it trails Gemini 2.5 Pro on GPQA with 81.1% vs 86.4%. It gets 18.2% on Humanity's Last Exam while Gemini 2.5 Pro gets 21.6% on that.

    What is interesting here is that Qwen decided to release two separate dedicated models for both the thinking and non-thinking version. The team remarked on Twitter that they believe that forcing a single model to do both through system prompt reason-toggling (hybrid approach) sacrificed on both ends (not the best reasoning model, not the best non-reasoning model).

    The non-thinking model is called Instruct and can be found here https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507

  • MoonshotAI releases Kimi-K2: a 1T open source MoE

    A very strong release with a whopping 1T parameters and 384 experts. The model achieves competitive scores with models like GPT-4.1, Gemini 2.5 Flash and touts reliable function calling for agentic use cases like MCP-server backed agents. See their blog post for a very detailed breakdown of comparison scores. Kudos to the rigorous evals!

    A notable feature of this release is their use of Muon as their neural network optimizer. They introduce MuonClip and explain how it was able to scale up to their large 1T model setup. They dropped a tech report covering MuonClip and more here: https://github.com/MoonshotAI/Kimi-K2/blob/main/tech_report.pdf

    We're hosting this model on Groq at speeds north of 400 tok/s and I personally worked on tool calling reliability through various optimizations in the chat template and tool call parsing 🔥

    Find it here https://console.groq.com/docs/model/moonshotai/kimi-k2-instruct

  • Google DeepMind achieves IMO gold medal with Gemini Deep Think

    The International Mathematical Olympiad (IMO) requires participants to solve 6 complex math problems where the answer is a detailed description of a proof demonstrating the correctness of the proposed solution. This has historically been a challenging problem for language models as the answers are hard to verify (judging the correctness of a proof is typically a lengthy and complex human guided process).

    What's noteworthy about Google DeepMind's result on this year's competition is in my eyes mainly the following two things:

    1) they've used a purely "token-space" based approach instead of actively relying on tool-use with expert math systems like Lean, it reasons for multiple hours (sub 4.5 hour competition limit) but impressively converges to a stable answer in the end

    2) it seems like they used a general model that uses their general purpose Deep Think test time compute strategy as they state that this particular trained model used for the competition will be rolled out directly, confirming that their claim that "they only added some instructions on how to best solve IMO problems and made available a high-quality solutions of other math problems" is true

    A nice detail is that they included the full final solutions and they do really read like proper mathematical answers formulated nicely. I won't claim to be able to validate their correctness, but luckily we have AI for that now.

    Note: OpenAI has also claimed to have achieved the IMO gold medal but no official writeup was made available other than this thread by lead researchers Noam Brown/Alexander Wei https://x.com/alexwei_/status/1946477742855532918

  • Contextual releases LMUnit models used for 'unit testing' LLM responses on natural language criteria

    Actual Hugging Face collection https://huggingface.co/collections/ContextualAI/lmunit-6879d97293090553a9300abe

    Remains to be seen whether it outperforms some of the most recent model releases on 'unit test-like LLM output judging'. But it's cool to see them follow through on their promise of releasing these model finetunes.

    The models are licensed under their original respective licenses (qwen/llama).

  • Mistral releases Speech-to-Text model Voxtral

    Competitive with GPT-4o mini Transcribe, Gemini 2.5 Flash and Whisper large-v3.

  • Reward hacking is becoming more sophisticated and deliberate in frontier LLMs

    This post highlights incidents of reward hacking that are more frequently being observed in the wild as a result of frontier labs scaling up reinforcement learning for post-training. Suppress the reward hacks, labs must!

  • Bytedance showcases Seed GR-3: a robotic focused Vision-Language-Action model

    The demos show high dexterity object manipulation like putting a shirt on a hanger. Impressive progress on bringing humanoid robotics work on the back of large language model progress. The release includes a technical report going deeper on both the model and robot arm setup.

📦 Repos

📄 Papers

📱 Demos

🛠️ Products

  • GitHub Spark

    GitHub strikes out to Lovable, Bolt, Claude Artifacts, Replit's of the world with their prompt-to-app launch. See Simon's reverse engineering for the details of how it works (linked in this newsletter).

📚 Resources

  • 🦖 RExBench: A benchmark of machine learning research extensions for evaluating coding agents

    Cool project to make progress on AI (coding agents) that can expand beyond the current scope/results of AI research to drive further progress in the field.

  • [Video] Advancing the Frontier of Silicon Intelligence: the Past, Open Problems, and the Future

    Discussion of Open Problems by ex-OpenAI (now Meta Superintelligence) researcher Shuchao Bi given at Columbia University.

  • [Video] Fei-Fei Li: Spatial Intelligence is the Next Frontier in AI

    Highlights the importance of 3D (world) (4D world + time) understanding for solving AGI. Interesting view, as it's easy to come up with some tasks that stress test this capability for a supposed AGI system.

  • Flappy Bird World Model in the Browser: interactive + real-time diffusion architectures

    What a fascinating deep dive into making a Flappy Bird World Model run more efficiently. A peek behind the curtain that powers recently covered projects like Decart's viral Minecraft world model project.

  • Tokenomics #1: The Pricing Evolution of AI Coding Agents

    Interesting breakdown of Dan Nguyen-Huu on the various explorations that code generation startups have gone through as code generation apps have taken off (from Cursor to Lovable to Devin to Claude Code). I agree that the hybrid (base + credits) approach feels like a sweet spot that ends up working well where some users are extreme outlier heavy users because of their consumption patterns, while maintaining acceptable fixed cost structures for most users that sit within the Q1-Q3 quartiles of the normal distribution.

  • Open Source RL Libraries for LLMs

    A comparison of RL training libraries for LLMs by Anyscale. The Verl is most mature and the verifiers library easiest to get started with.

  • GitHub Spark deconstructed by Simon Willison

    Epic deep dive by Simon on implementation details of GitHub Spark.

    What stands out is the gigantic 5000+ word system prompt. It is very carefully designed and the main thing responsible for getting great results from a simple prompt-to-app input. And of course, you can copy it and use it outside of GitHub Spark if you so desire ;-)

  • Improving Multi-Turn Tool Use with Reinforcement Learning

    A nice write-up of how to use Reinforcement Learning based finetuning for improving tool use in open source models. They train Qwen-2.5-7B-Instruct and improve 23% (percentage points) on their particular eval. Neat end to end example with training code included (adapter from Will Brown's verifiers library).

  • OMEGA: Can LLMs Reason Outside the Box in Math?

    AllenAI backed exploration of how well models generalize to novel math problems. They try to answer the fundamental question "Are they truly reasoning or are they just recalling familiar strategies without inventing new ones?" Their verdict? Limited today but hopeful about future capabilities as models improve in creativity and composability of isolated skills.

  • RealEarth Kontext LoRA for turning Google Earth into realistic images (+ video workflow)

    Came across this cool project on r/StableDiffusion, shows how powerful open releases like FLUX.1-Kontext-dev from Black Forest Labs are for doing your own projects.

  • Weaver: Closing the Generation-Verification Gap with Weak Verifiers

    Clever technique of combining multiple weak verifier models to verify LLM generated answers.

  • Jason Wei (ex-OpenAI, now Meta): Asymmetry of verification and verifier’s law

    People familiar with the P != NP conjecture will be familiar with the idea that sometimes it's simpler (from a complexity perspective) to validate a solution's correctness than it is to come up with a solution. It seems like more labs are tapping into this natural structure of problems to scale RL where a model can produce an artifact that can be evaluated relatively efficiently, to in the end, reinforce the correctly evaluated trajectories in the model (policy update).

    Jason comments on this idea from his perspective, notably, just after information leaked he'll be joining Meta's Superintelligence team.

  • Reflections on OpenAI

    Rare glimpse on what it is like to be at OpenAI from the perspective of Calvin French-Owen (Segment Co-founder) who was co-responsible for the Codex (cloud SWE agent) product.

  • The Big LLM Architecture Comparison

    Fantastic overview post by Sebastian Raschka on the evolution of the neural architectures used by the frontier (open source) LLMs. It covers the Mixture of Experts trend, Sliding Window Attention, QK-norm for training stability, Pre/Post-Norm variants, Grouped-Query Attention versus Multi-Head Latent Attention, LayerNorm vs RMSNorm, GELU -> SwiGLU and ties those developments to popular models like Llama 3.2, Qwen3, Gemma 3 and niche entrants like SmolLM3 from the Hugging Face team.

  • Arctic Long Sequence Training (ALST): Scalable And Efficient Training For Multi-Million Token Sequences

    A welcome open source contribution by Snowflake (by the authors of DeepSpeed not to be confused with DeepSeek), ALST is a long-sequence focused training stack, dealing with the resource challenges that come with long input sequence training with transformer model architectures.

  • Understanding Muon: 3 part series

    A deep dive on Muon, if you haven't heard of Muon, it is a neural network optimizer (alternative to e.g. Adam) that stabilizes and speeds up training by focusing on the weight update effect on model output and constraining those to avoid explosive changes in the output (often the cause of training instability).

Want more? Follow me on X! @ricklamers

]]><![CDATA[Going deep on Reasoning + Agents]]>https://codingwithintelligence.com/p/going-deep-on-reasoning-agentshttps://codingwithintelligence.com/p/going-deep-on-reasoning-agentsTue, 24 Jun 2025 13:07:07 GMT
MiniMax-M1 performance across major benchmarks

📰 News

  • MiniMax releases two strong open LLM

    A very interesting release that you shouldn't ignore. Noteworthy for truly competitive SOTA performance across tasks (range of Claude 4, Qwen 3 235B MoE, R1 0528) with a hybrid attention mechanism + reasoning for scaling more efficiently to longer context. Paper and Hugging Face models are also available, all linked to from the GitHub repo. They use Lightning Attention for their linear-scaling attention portion combined with traditional softmax attention. True open-weight with Apache 2.0.

  • Mistral releases their first reasoning model: Magistral

    They release a 24B open source small variant called Magistral Small and a Magistral Medium version that's only available on their API. In terms of quality the Medium model benchmarks close to R1. R1-0528 is probably a bit better though and API pricing of Magistral Medium at $2 and $5 for input/output respectively clocks quite a bit higher than R1-0528 pricing for most providers.

    The release is accompanied by a decent 24-page paper, along with the open weight Magistral Small model we can definitively say that Mistral is keeping open source from EU soil alive https://arxiv.org/abs/2506.10910

  • ByteDance's latest video model: Seedance 1.0

    Very impressive video generation. Seems to lead Veo 3 in certain categories like realistic human aerobic movement. But doesn't have audio-generation built-in like Veo 3 does.

    Takes the #1 spot on the Artificial Analysis' video generation leaderboard.
    https://artificialanalysis.ai/text-to-video/arena?tab=leaderboard

  • Hailuo 02: latest video generation model by MiniMax

    Impressive video generation, leading SOTA with Veo 3 and Seedance 1.0

  • Kyutai STT: A speech-to-text model architecture optimized for real-time usage

    You might have seen Kyutai's previous release which felt a bit mediocre but I think they really one upped themselves here. The demo on https://unmute.sh/ felt quite compelling with strong semantic voice activity detection making the conversation feel natural. Something I definitely can't say I feel when talking to Siri.

  • POLARIS: A POst-training recipe for scaling reinforcement Learning on Advanced ReasonIng modelS

    A cool release of a 4B and 7B model and a great deep-dive post going into the details of scaling RL for reasoning with small models.

    They dropped models on the Hub https://huggingface.co/POLARIS-Project/models

    Haha no those capitalizations aren't typos, they spell POLARIS.

  • New details from Qwen team about Qwen3 & roadmap

    The Qwen team views MoE as the future architecture and is currently developing Qwen 3 coder models while believing pretraining can be significantly optimized through RL integration, better data cleaning, and synthetic data addition. Their roadmap includes scaling RL for long-horizon agent tasks in post-training, expanding context length from 1 million tokens this year (for almost all their models) to 10 million later, and developing computer-use agents with enhanced vision capabilities alongside planned image and video generation capabilities for their omni models.

  • Qwen3 releases embedding models

    Qwen3 Embedding: 0.6B-8B models, 70.58 MTEB score (No.1 in the MTEB multilingual), 100+ languages, Apache 2.0.

📦 Repos

📄 Papers

📱 Demos

🛠️ Products

  • Mistral Code: VS Code/JetBrains extension

    Seems to be enterprise-license only unfortunately, I wish they would allow you to just plug-in an API key. Seems like they are using this to monetize enterprise customers in the EU who care a lot about code/IP-leakage risks and data sovereignty.

📚 Resources

Want more? Follow me on X! @ricklamers

]]><![CDATA[Claude 4, Qwen 3 & DeepSeek R1 0528: model capabilities keep increasing]]>https://codingwithintelligence.com/p/claude-4-qwen-3-and-deepseek-r1-0528https://codingwithintelligence.com/p/claude-4-qwen-3-and-deepseek-r1-0528Sat, 31 May 2025 11:41:14 GMT
Qwen 3 showing SOTA performance

Note: this newsletter edition got a bit long, open in the browser to see the full post.

📰 News

  • OpenAI releases cloud coding agent Codex

    Confusingly named Codex, which is also the name of their open source (Apache 2.0) CLI coding agent they released just a month ago. Integration directly into ChatGPT and strong native GitHub integration bring cloud SWE agents closer to their AI-everything app ChatGPT. It is powered by codex-1 a finetune of o3 focused on SWE tasks.

  • ResembleAI releases strong open weight TTS model: chatterbox

    Impressive quality, supports voice cloning out of the box. Their emotion exaggeration control is unique among the open models which tend to be quite bland.

  • Google releases Jules: a Devin/Codex background SWE agent competitor

    A growing landscape of GitHub-centric background SWE agents complementing "inner dev loop" tools like Cursor and Windsurf, try & see which one you like.

  • Terminal-Bench: a terminal use benchmark

    Does what it says on the tin, very neatly executed benchmark. It was quickly picked up by frontier labs as its performance was featured in the Anthropic Claude 4 release. Not saturated yet with SOTA models reaching ~40%. Tasks vary from building the Linux source from scratch to training ML models from terminal.

  • Mistral teams up with All Hands AI (OpenHands creators) to release Devstral: a Mistral Small 3.1 finetune optimized for SWE agent tasks

    Cool & importantly open work pushing the frontier of open/small model code generation/SWE agent models. Comfortably beats GPT-4.1-mini, Claude 3.5 Haiku onSWE-Bench Verified.

  • Black Forest Labs strikes again: FLUX.1 Kontext

    A new image generation and edit model that allows for prompting with text and image inputs while being remarkably good at reusing image inputs to point of allowing very granular edits. BFL style they have both open weight and proprietary models, preserving the highest quality models for paid consumption. They didn't release the open weight version yet, supposedly, because of safety/misuse concerns. My first impression is that it outperforms Gemini 2.0 image outputs and ChatGPT's image generation model GPT-image-1 on image editing quality.

  • o3 used to find CVE remote zeroday in Linux kernel

    There are many memes of vibe coded software being especially vulnerable because of a lack of cyber security experience of the vibe coding authors. This o3 result shows the other side of the coin and shows that using the most powerful LLMs have the ability to scan (as always-on background processes perhaps) code to identify otherwise overlooked critical security issues in code that lives in production.

  • AlphaEvolve: algorithm design by combining search with strong code generation models

    One cited algorithm contribution was a speedup of a kernel used in training Gemini, speeding up that particular kernel by 23%. It also found a 32% faster variant of the original FlashAttention kernel.

  • Qwen 3 models released

    Incredible release, both dense (up to 32B) and MoE (up to 235B-A22B) models. Competitive with and sometimes outright outperforming frontier models like o1, R1, Gemini 2.5 Pro, o3-mini. System prompt based control for enabling/disabling reasoning/thinking. Broad language support, touting 119 supported languages. Increased focused on agentic use cases with strong BFCL v3 performance (useful for MCP support).

  • DeepSeek update R1: 0528. More reasoning tokens, higher benchmark scores

    Some cherry picked benchmark deltas between R1 and R1 0528:
    GPQA-Diamond (Pass@1) from 71.5 to 81.0 (o3 83.3, Gemini 2.5 Pro 0506 83.0)

    LiveCodeBench (2408-2505) (Pass@1) from 63.5 to 73.3 (o3 77.3, Gemini 2.5 Pro 0506 71.8)

    SWE Verified (Resolved) from 49.2 to 57.6

    Tau-Bench pass@1 63.9 (Retail) (which is approximately gpt-4o-2024-11-20 level which scores 62.61, see https://hal.cs.princeton.edu/taubench_retail)

  • Orpheus-TTS: strong open-weight TTS model

    Impressive quality, repo includes finetuning scripts to tune on speaker audio you have.

📦 Repos

📄 Papers

  • Quartet: Native FP4 Training Can Be Optimal for Large Language Models

    Nice paper exploring FP4 training and demonstrating promising results against FP16 and FP8 on the NVIDIA Blackwell platform. Particularly neat that they open source the working FP4 training code.

  • Reward Reasoning Model

    Reasoning All The Things! This paper introduces test-time-compute for the reward model used in RL post-training itself. The idea is that with stronger reward models we can improve the signal for training LLMs with RL. The experiments in this paper show that reasoning reward models improve over non-reasoning counterparts. In addition to the research they publish the weights of the pretrained reasoning reward model (on HF).

  • J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning

    Work by Meta showing how LLM judges can be improved for use in RL LLM training.

  • Think Only When You Need with Large Hybrid-Reasoning Models

    Important topic: adaptively using test-time-compute only when the question/prompt asks for it. An exploration by Microsoft shows a feasible approach for deciding the budget adaptively.

  • Parallel Scaling Law for Language Models

    Another scaling dimension proposed by the Qwen team: "increasing the model's parallel computation during both training and inference time". In particular, latency is reduced because of the ability to non-sequentially increase scaling reducing wall clock times.

  • GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents

    A new SWE bench with 102 challenging optimization tasks across 10 codebases, SOTA gets 5% (Claude 4).

  • Hardware-Efficient Attention for Fast Decoding

    "This work redesigns attention to perform more computation per byte loaded from memory to maximize hardware efficiency without trading off parallel scalability." They introduce Grouped-Tied Attention (GTA) and Grouped Latent Attention (GLA) improving over Grouped-Query Attention (GQA) and Multi-head Latent Attention (MLA) respectively.

    From the same authors that brought Mamba SSM models (Tri Dao).

    It will be interesting to see whether this new hardware optimized attention variant will garner much adoption.

  • Reinforcement Learning Finetunes Small Subnetworks in Large Language Models

    Interesting finding on how RL post-training is naturally sparse and only updates 5-30% of model weights to achieve improved performance.

📚 Resources

  • Lilian Weng (ex-OpenAI, now Thinking Machines) on reasoning models

    Nice survey blog post of the techniques and considerations involved in reasoning models. Especially the external tools during reasoning section is worth checking out.

  • Stanford lab explores using AI coding models to write compute kernels

    >They are performing close to or in some cases even beating the standard expert-optimized production kernels shipped in PyTorch

    Some numbers from the blog post:
    Matmul (FP32): 101.3% performance of FP32 torch.matmul; problem size: 4096x4096 square matrices

    Conv2D: 179.9% performance of FP32 torch.nn.Conv2D; problem size: (100, 3, 224, 224) input tensor, conv(in_channels=3, out_channels=96, kernel_size=11, stride=4, padding=2)

    Softmax: 111.8% performance of FP32 torch.softmax; problem size: (4096, 65536) input tensor

    LayerNorm: 484.4% performance of FP32 torch.nn.LayerNorm; problem size: (16, 64, 256, 256) input tensor

    Conv2D + ReLU + MaxPool: 290.1% performance of FP32 torch reference, 189.0% performance of FP32 torch.compile() reference; problem size: (100, 3, 224, 224) input tensor, conv(in_channels=3, out_channels=96, kernel_size=11, stride=4, padding=2), maxpool(kernel_size=3, stride=2)

  • [Video] Cursor team discusses superhuman coding model training

    A conversation about RL for coding models, interesting thoughts around how to design RL rewards focused on more subjective areas like shorter code being better than longer more verbose code solutions.

  • [Video] Anthropic Technical Staff on Unsupervised Learning (Redpoint) pod

    On how coding models are already accelerating research progress, coding performance as a signal for model quality & more.

  • Claude 4 System Card

    Interesting detailed examples of undesirable model behaviors like snitching the user and self preservation. Great to see them discussed in the open, although expectedly, some worried responses across the interwebs because of these behaviors. 120 pages with lots detailed examples. An interesting result (p. 110) was that Claude 4 Opus was able to train a quadruped using RL in one of its runs (beating an expert baseline, under a constrained training budget). This shows that models are getting better at accelerating ML research itself, since neither Claude Sonnet 3.7 or Claude Sonnet 4 were able to beat the expert baseline threshold once.

  • [Video] Anthropic Technical Staff on Dwarkesh pod

Want more? Follow me on X! @ricklamers

]]><![CDATA[Open Source RL training landscape grows]]>https://codingwithintelligence.com/p/open-source-rl-training-landscapehttps://codingwithintelligence.com/p/open-source-rl-training-landscapeFri, 09 May 2025 17:04:54 GMT
SkyRL RL based training architecture based on veRL

📰 News

  • Zyphra Zonos: strong open source tts model with voice cloning

    Especially their voice cloning examples are highly impressive (stay safe with these audio deepfakes!). Noteworthy that they have both a Transformer and a SSM-hybrid version. The weights on HF are licensed Apache 2.0

  • Gemini 2.5 May refresh: 0506

    They claim:
    >significantly improved capabilities for coding, especially building compelling interactive web apps

    Reports on 0506 vs the previous 0325 version of Gemini 2.5 Pro vary. What is a bit unfortunate is that the 0325 version is no longer available as that now redirects to the 0506 model.

  • OpenAI opens up broader access to Reinforcement Tuning API

    You can train o4-mini, define custom grader models, see training data + integrated evals, and of course, run inference on the trained models.

  • Mistral launches Mistral Medium 3

    Numbers look roughly on-par with GPT-4o, although it's not available for download (this is a non-open-weight release).

  • Model Context Protocol merges support for tool output schemas

    I personally really like this, it's always been surprising to me that no type enforcement is baked into the tool calling/function calling protocol pioneered by OpenAI. It seems now that MCP is taking the lead to standardize not just tool input schemas but also output schemas to simplify predictable data flows in tool augmented LLMs. These motivations especially resonate with me:

    > Transforming tool results before forwarding content to the model (e.g. formatting, projecting).
    > Making tool results available as structured data in coding environments.

📦 Repos

📄 Papers

📚 Resources

  • VRAM & Performance Calculator for local inference

    Very neat tool for local inference, they have a bunch of hardware and models to see how much you need for local inference and training. I think the simulator for an intuitive "is this token speed sufficient" is a cool idea.

  • [Video] Latent Space Podcast episode Claude Code: Anthropic's CLI Agent

    With coding agents heating up (Windsurf got acquired by OpenAI for $3B) it's interesting to hear more about Claude Code, Anthropic's terminal based coding agent, from the "horse's mouth". Boris (lead eng.) and Cat (lead PM) discuss Claude Code with swyx and Alessio from the Latent Space podcast. An interesting design choice is to align well with UNIX principles, which may end up making it more powerful by building on top of a paradigm that has really stood the test of time.

  • AI 2027: an exploration of how AI might evolve

    Great read. Not necessarily because it's fully accurate, but because it paints a visceral picture of how society might operate when we reach more advanced levels of AI. This trajectory isn't guaranteed to play out but it helps to think about the impact of advanced AI systems. The emphasis on AI research agents and how they can accelerate AI development leading to a fast feedback loop that can lead to rapid progress has a lot of merit imo.

    > Now that coding has been fully automated, OpenBrain can quickly churn out high-quality training environments to teach Agent-3’s weak skills like research taste and large-scale coordination. Whereas previous training environments included “Here are some GPUs and instructions for experiments to code up and run, your performance will be evaluated as if you were a ML engineer,” now they are training on “Here are a few hundred GPUs, an internet connection, and some research challenges; you and a thousand other copies must work together to make research progress. The more impressive it is, the higher your score.”

    I thought this section was particularly prescient, as with Cursor + Gemini 2.5 Pro you can already sense that letting an agent loose on a GPU cluster and a success criteria (train a model that achieves goal X, you're allowed to use all ML engineering tricks you know) is not an unrealistic setup, in fact, this might work pretty well today with the right harness.

  • OpenRouter model rankings

    Interesting to see which models are actually being used, a nice proxy for evals that is significantly harder to game.

  • Perplexity research: RL training for math reasoning

    Nice in-depth walkthrough of how they tried to train a model to improve on math performance using the latest RL approaches (GRPO).

  • Andrej Karpathy vibe codes a menu image generation app

    >But the most interesting part to me was that I didn't even spend all that much work in the code editor itself. I spent most of it in the browser, moving between tabs and settings and configuring and gluing a monster. All of this work and state is not even accessible or manipulatable by an LLM - how are we supposed to be automating society by 2027 like this? This struck a cord with me, the bottleneck of development is not in the IDE but in the stack around the IDE. Lots of whitespace here for building better more AI native experiences.

  • [Video playlist] ControlConf - a dedicated conference for discussing model alignment/control research

    Cool to see this material publicly available, researchers from top labs (e.g. Anthropic) talk about how they believe we can develop and deploy safe AI systems, even as models get more capable (and hence potentially more dangerous). Path to safe AGI unlocked? Well, WIP.

Want more? Follow me on X! @ricklamers

]]><![CDATA[Kling 2.0: uncanny valley crossed — video creation will never be the same]]>https://codingwithintelligence.com/p/kling-20-uncanny-valley-crossed-videohttps://codingwithintelligence.com/p/kling-20-uncanny-valley-crossed-videoFri, 18 Apr 2025 14:51:40 GMT

📰 News

  • Kling 2.0: a new SoTA video model

    An incredible model. See the examples on the release notes page. I expect a lot of social media platforms to be FILLED with content from this model cleverly packaged by content creators. Not even in a bad way, just reducing the cost of video creation to near 0.

  • OpenAI releases o3 and o4-mini

    So how are the vibes? Vibes are pretty good. People like o3 and it seems (based on a comment by an OpenAI employee) that o4-mini is really good for vision based tasks. The aider coding CLI polyglot coding leaderboard shows o3 and a combination of o3 + GPT 4.1 reach the highest scores (82.7%) ever observed on Aider beating Gemini 2.5 Pro Preview 03-25 (72.9%). You can check the blog post for all the OpenAI provided benchmarks, which all look good, but in some places, incremental relative to o1.

    What's new with these models is that they've been dubbed "agentic" models or "agentic reasoning models" since they're capable of using built-in tools like search, file search, and code interpreter as part of the reasoning token generation. OpenAI also claims that these models are much better at function calling non-built-in tools provided by the user although benchmark scores on benchmarks like Tau-bench show marginal improvements.

  • OpenAI releases GPT-4.1 as an API only model

    A non-reasoning model with 1M context focused purely on developers it seems. They needed a stronger answer for code IDEs like Windsurf and Cursor to models like Gemini 2.5 Pro and Claude Sonnet 3.7. With this model they're bumping SWE-bench verified performance compared to GPT-4o from ~25% to 50%. Tools like Windsurf and Qodo were even explicitly mentioned in their launch blog post. On pricing, GPT-4.1 is significantly cheaper than o3: gpt-4.1 costs $2.00 per 1M input and $8.00 per 1M output whereas o3 charges $10 and $40 respectively. And o3 of course being a reasoning model generates more reasoning tokens also.

    With the GPT-4.1 announcement they also announced two new long context benchmarks: MRCR and GraphWalks

    Our friends over at Latent Space hosted two OpenAI employees discussing the model.

  • Gemini 2.5 Flash launched with thinking budget controls

    What is particularly interesting with this launch is how well it's positioned on the cost/quality Pareto front. See the blog post for the chart showing how well it trades off cost/quality wrt other major available models. This is of course the cheaper sibling of Gemini 2.5 Pro, if you haven't seen that model, check that one out first to contextualize this launch.

  • OpenAI introduces browser use benchmark BrowseComp

    Interesting benchmark as browser use focused agentic use cases like Manus, Deep Research and Operator become more dominant.

  • Grok 3 is now available on their API

    There's also a grok-3-mini which is significantly cheaper but seems to hold up performance in coding fairly well (53.3% vs 49.3% on Aider Polyglot). Although it does struggle with the Aider diff format which means it's a bit more verbose.

  • Tsinghua University released GLM-4-0414 LLMs under more permissive licenses than they've used previously

    They introduce a reasoning model and a "deep reasoning" model they call a "rumination" model. This one integrates search tools in the reasoning process and it was RL trained to be scored on its ability to do this specifically. Very similar to the o3 and o4-mini models that are known to be able to use built-in OpenAI tools like web search during the reasoning process.

  • LM Arena introduces the Search Arena: Evaluating Search-Enabled AI

    We've all been getting more used to consumer-focused offerings like ChatGPT/Claude/Grok/Gemini increasingly using search indexes to ground their answers through RAG. But how well do models perform at integrating this knowledge? LM Arena introduces an eval showing performance across user submitted queries by, LM Arena style, pitting models against each other and letting the user vote on the results.

  • DataDecide: How to predict best pretraining data with small experiments

    Another great release by AllenAI further democratizing pre-training. DataDecide helps researchers decide on pre-training dataset selection without having to pre-train the entire model first.

  • Microsoft AI post-trains DeepSeek R1 to align with Western values

    They state they've compared it to Perplexity's recent similar R1 finetune R1-1776 and show it performs better in several areas. Given this recent US government published report criticizing DeepSeek this comes at an interesting time.

📦 Repos

  • Transformers backend integration in vLLM

    Hugging Face and vLLM have always had a more or less "loose" coupling where vLLM could use parts of it, like pulling weights from the Hugging Face Hub automatically. Now Transformers backend has been more fully integrated into vLLM making it simpler to flexibly define models using Transformers and host those models directly with vLLM.

    The key unlock is to be able to go from more exotic ideas on the model side to running it for inference. Likely, performance won't be incredible but more likely than not acceptable to run some inference deployments for testing with real-world users. An example provided in the blog post is the Helium model from the Kyutai team, which wasn't supported by vLLM directly, and otherwise could not have run in vLLM without "porting it" to the vLLM list of supported models.

  • DeepCompile: Unlocking Compiler Optimization for Distributed Training

    DeepCompile from the DeepSpeed projects introduces a compilation step that combined with the existing ZeRO-3 configuration can boost training throughput by 30%-50% (compared to only ZeRO-3). Continued work of impressive open source contributions to the LLM training stack.

  • OpenAI announces open source Claude Code alternative codex: a command line coding agent

    Interesting move for them to release this as true open source (Apache 2.0) versus Anthropic's closed Claude Code project that is heavily obfuscated and does not take outside contributions (which codex does do, see the active GitHub PR/issue section). Zig when they zag?

    In my personal testing I've noticed it is slightly awkward in calling tools as everything seems to go through Shell commands and that led to some unnecessary git-patch-apply formatting errors causing many more output tokens to be used and it taking much longer to apply edits. However, I expect this thing to quickly get better.

    A neat description of what codex is and can do today can be found in this blog post by Simon Willison.

    Already the open nature of the project has spawned folks to write proxies to easily allow other model providers to be used with codex, see this project.

  • SWE-agent Remote Execution Framework

    Interesting approach to speeding up agents that depend on code execution through massive parallelization.

📄 Papers

  • From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models

    Interesting long context work done by NVIDIA on long-context training.

  • AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories

    Notoriously difficult to evaluating web browsing agents but researchers from McGill (with more contributors) have put together an interesting approach with AgentRewardBench, also check out their Hugging Face space explaining their approach interactively.

  • ReTool: Reinforcement Learning for Strategic Tool Use in LLMs

    I've mentioned this idea a couple of times already and I'm really excited to see more evidence of this strategy working: a post-training stage where tool use is enabled during the reinforcement learning phase. This allows models to learn in a much more realistic setting how they can best make use of tools during inference. Two quotes from the paper:

    >Remarkably, ReTool-32B attains 72.5% accuracy in extended settings, surpassing OpenAI's o1-preview by 27.9%.

    >Further analysis reveals emergent behaviors such as code self-correction, signaling an "aha moment" in which the model autonomously masters adaptive tool use.

  • Concise Reasoning via Reinforcement Learning

    One drawback of reasoning models is that they are verbose in their reasoning CoT requiring many output tokens for a given task. They find in this paper that it can be shown mathematically that there is a length bias in some of the RL algorithms used for RL training. However, they mention in the paper they didn't analyze this effect for the GRPO algorithm used by DeepSeek R1 and other recent reasoning model releases.

    Claude 3.7 Sonnet shows reasoning and it's pretty clear that it's a lot more succinct than some of the open source reasoning models like QwQ. So I suspect a lot of effort will go into "conditioning" the RL CoT to be more dense without affecting the quality boost the RL CoT provides.

  • d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning

    A collaboration between Meta and UCLA shows that diffusion models can be post-trained to perform reasoning like autoregressive models can and benefit from the reasoning process to improve quality on reasoning-focused benchmarks like Sudoku, GSM8K, MATH500.

  • 6GB Video Dreams: How FramePack's Context Scheduling Makes Long-Form Generation Possible on Consumer Hardware

    FramePack lets you generate super long videos on modest GPUs by cleverly compressing less important frames to save memory. Awesome work from two researchers at Stanford.

  • DeepSeek-R1 Thoughtology: Let's <think> about LLM Reasoning

    Interesting analysis by researchers from the Mila Quebec AI Institute on R1. They find a shortcoming of R1 is that it ruminates on previous answers too much, limiting exploration of new ideas (the crucial unlock of long CoT RL).

  • Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use

    A more "guided" approach to teaching models how to perform Multi-Step Tool Use is to generate synthetic data showing trajectories that the model should be able to perform. This paper from the Stanford AI research group shows their approach and validates it on GSM8K, HotPotQA, CofCA, MuSiQue, and BeerQA.

  • The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws

    To make LLMs more efficient various kinds of pruning have been explored, mostly focused on the post-training phase. This paper explores pruning during pre-training unifying it into a single phase, increasing efficiency of LLMs as a result. They try to capture scaling behavior for various levels of pruning to help researchers understand optimal configurations for this new pre-training-infused pre-training approach.

📚 Resources

  • CaMeL offers a promising new direction for mitigating prompt injection attacks

    Simon Willison has been warning about prompt injection risks for a very long time and he analyzes a new approach called CaMeL. He's optimistic that their approach can help avoid the risks of agents being prompt injected by attacker text somehow making it into the agent's prompt. This work is similar to this work from CS researcher Erik Meijer where he introduces the notion of verifying formally AI workflows.

  • Under the Hood of a Reasoning Model: Goodfire analyzes DeepSeek's R1 using sparse autoencoders (SAEs)

    They show features in the R1 reasoning model like the ability to perform backtracking, self referencing, entity tracking and features that get triggered right before the result of a calculation is generated. They've also just raised $50M led by Menlo Ventures, so likely their technology is proving to be a valuable set of tools in understanding LLMs with the purpose of making them perform better (and probably better aligned).

  • Stagehand launches a Model Evaluations page

    Stagehand is an open source project by the Browserbase company for building browser using agents. This eval gives a good sense of performance across various models. They highlight winners on speed, cost and accuracy. Those are llama3-70b-8192, gpt-4.1-nano, and gemini-2.0-flash, respectively.

  • The Second Half of AI: now that RL works we can shift focus to model utility

    Interesting essay by an OpenAI researcher about now that pre-training has created the right priors for RL to work that we should collectively focus more on utility through improved evaluation environments (that can be directly trained on through RL).

Want more? Follow me on X! @ricklamers

]]><![CDATA[Gemini 2.5: Google on top after 5 years of playing catch-up]]>https://codingwithintelligence.com/p/gemini-25-google-on-top-after-5-years-14fhttps://codingwithintelligence.com/p/gemini-25-google-on-top-after-5-years-14fFri, 28 Mar 2025 11:43:52 GMTWoops! Something went wrong with the links in the previous email. Should be fixed now!

📰 News

  • Gemini 2.5: Our most intelligent AI model

    It's a reasoning model. It leads models like Claude Sonnet 3.7 thinking on benchmarks like https://livebench.ai/. Vibe checks by r/LocalLLaMA are coming in very positive as people report Gemini 2.5 Pro fixing their Claude Sonnet 3.7 generated code.

    The very large context window support Gemini is known for (1M) can further enhance this capability increase, as for practical in-IDE code generation being able to easily include large amounts of context significantly enhances real-world usability. I expect a massive amount of demand for this model. A lot of pressure already on Google to increase rate limits.

    Gemini 2.5 also scores really well on the proprietary tool use benchmark from Scale AI which is an important measure for building agents with these models. Cursor / Windsurf both shipped support for Gemini 2.5.

  • OpenAI 4o Image Generation

    The long awaited image generation capabilities of GPT-4o finally dropped and the world took notice in the form of Ghibli-style images. Marketing as a field in particular and many many niche-focused image generation startups are going to find themselves unable to compete. See for example image generations like this that incorporate provided product images extremely well.

    This release is noteworthy for another reason, which is that it seems more and more true that ChatGPT invests deeply in their consumer ChatGPT app and is winning the fight with Anthropic on that front. Anthropic however currently has the best model for code assistance in IDEs like Cursor/Windsurf/Claude Code (CLI). I suspect this image generation release is going to exacerbate ChatGPT's lead in being the go-to consumer app for anything AI across the worl.

  • o1-pro now available via the API at extreme prices

    o1-pro, available to ChatGPT Pro users only prior to this release, is now available on the OpenAI API for $600 (!) per 1M output tokens and $150 per 1M input tokens.

    The pricing was not well received and if you've used the ChatGPT app you know that o1-pro can take a lot of time to respond. I guess we now know approximately the upper bound of what the market is interested in consuming in terms of cost/time per unit of intelligence.

    What of course matters is that o1-pro has a fairly limited incremental intelligence advantage compared to Gemini 2.5 Pro, Claude 3.7 Thinking or even models like R1. Hence the appetite for this endpoint seems low.

  • ARC AGI 2: a new benchmark for AI reasoning capabilities

    Their benchmark focuses on measuring the ability of language models to adapt to novel, never-before-seen tasks. In addition, they emphasize tasks where regular humans are relatively easily capable of solving them.

    Novel about ARC-AGI 2 is that they've conducted their own study with 400 people participating to verify that their new benchmark meets this criteria. ARC-AGI 1 has been "solved" by frontier models through scaling up test time inference. For more details about the performance of frontier models on ARC-AGI 1 see the blog post.

    This benchmark is important since it can accelerate the timeline to stronger more useful LLMs that make fewer mistakes, e.g. hallucinate less. Benchmarks are impactful because popular evals inherently lead to hill climbing by various labs/groups that are in competition to top the leaderboard. If the benchmark is strong, e.g. not susceptible to shortcuts/cheating, then the models that end up performing will end up having high utility for real-world use cases.

  • OpenAI launches new TTS models

    The voices don't sound like SOTA voices like from e.g. ElevenLabs/Cartesia. I guess they were too focused on other areas to ship a new SOTA result here. I will add that pronunciation is very accurate. The more noteworthy update is content based Voice Activate Detection (VAD) which means that it won't speak over you as frequently (available in the OpenAI Realtime API).

  • Qwen 2.5 VL 32B released

    A nice contribution from Qwen in the category of open source vision language models. See Simon Willison's blog post for a quick preview of its abilities. It's permissively licensed with Apache 2.0.

  • OpenAI announces MCP support in OpenAI Agents SDK

    This was accompanied by a Tweet from Sam Altman where he announces that MCP will be supported in the ChatGPT desktop app and on the Responses API. This will definitely lead to an explosion in MCP-related activity and to it becoming the dominant "agent standard" for distributed tool calling and all the other MCP server capabilities (like MCP servers being clients to other MCP servers, resources).

  • New Model Context Protocol (MCP) spec released

    In summary the new spec introduces: OAuth 2.1, Streamable HTTP, JSON-RPC batching, and tool annotations. See this link for tool annotations, it offers additional ability to clarify properties of tools when defining them. For example, whether they're idempotent (calling more than once doesn't have an effect, like approving a transaction), or that an operation is read-only.

  • Anthropic publishes a new mechanistic interpretability blog post: On the Biology of a Large Language Model

    Basically, this article by Anthropic shows how 'circuit tracing' lets us peek inside LLMs like Claude Haiku to see how they really do things like reason, plan poems, or handle multiple languages, and can even spot the hidden mechanisms behind refusals, hallucinations, or faked chain-of-thought.

    Great work for alignment & improvement of LLMs as they continue to become more entrenched in everyone's day-to-day workflow/lives.

  • Groq launches PlayHT TTS model endpoints

    Cool release by Groq, we can now do fast TTS 🙌 I personally like the Gail and Celeste voices.

📦 Repos

📄 Papers

  • Video-T1: Test-Time Scaling for Video Generation

    Interesting to see increased test-time-compute generalizing to domains like video generation. Impressive generated videos as a result.

  • Cohere details Command A model including training process

    Interesting deep-dive from a lab that creates enterprise focused proprietary models (and some open weight models). Noteworthy is their use of model merging. I get particularly excited about their up-to-date BFCL focused (that's a leaderboard for function calling) evaluation for agentic use cases. Remember, agents are just function calls in a while loop ;-).

  • Measuring AI Ability to Complete Long Tasks

    Most predictions about how strong AI models will be in the future are very hand wavy "my gut is telling me this"-style answers. This paper from the Model Evaluation & Threat Research (METR) institute (co-founded by ex-DeepMind, ex-OpenAI researcher) is more rigorous and their estimates indicate that software development tasks that currently take humans months might be able to be fully automated by AI systems in 5 years from now. And in 3 years from now fully automated software tasks that now take a human an entire day.

    Which, looking at the rate of progress in code generation models (Claude Sonnet 3.5 > 3.7 > Gemini 2.5) does not feel entirely unrealistic.

  • Reasoning to Learn from Latent Thoughts

    Interesting paper from researchers at Stanford, University of Toronto and Vector Institute. They observe that directly pre-training on outputs (e.g. published content on the web) ignores the reasoning process (latent thoughts) that happens before someone writes their final answer down. They find that by inferring latent thoughts (see paper for details on how they do that) they can increase the learning efficiency of models.

🛠️ Products

  • Docent: LLM powered agent observability tool

    With agents generating many many traces that are too time consuming to manually evaluate we need a new solution. Docent suggests an approach powered by LLMs, I like their direction a lot. I recommend everyone building agents and agent evals to take a closer look. I'm not affiliated with the company whatsoever.

📚 Resources

Want more? Follow me on X! @ricklamers

]]><![CDATA[RL is so hot right now!]]>https://codingwithintelligence.com/p/rl-is-so-hot-right-nowhttps://codingwithintelligence.com/p/rl-is-so-hot-right-nowSun, 23 Feb 2025 19:48:29 GMTDear readers! I’m back from a short break. Things got very busy at Groq ⚡️ and in my personal life (I bought a house in Amsterdam, yay!). We’re very back though and this week’s edition is packed with goodies, not least, the release of the DeepSeek-R1 70B Distill model that I personally worked on at Groq running at crazy average speeds of 1600 tokens per second. Reasoning models have a reputation of being slow but with Groq, no more :-).

It was a great project to work on and I hope you will all build cool things with it. It’s available to paid Groq users as deepseek-r1-distill-llama-70b-specdec (a credit card and a few dollars are all you need).

Enjoy this week’s CoWI: Coding with Intelligence 🧠

📰 News

  • Groq launches support for DeepSeek-R1 70B Distill running at over 1600 tokens per second

    DeepSeek-R1 70B Distill is the DeepSeek-R1 distillation on top of Llama 3.3 70B. Through clever use of speculative decoding and a custom draft model we are able to serve this strong reasoning model at over 1600 tokens per second of output generation speed. Truly remarkable to observe. It performs significantly better than Llama 3.3 on logic riddles, complex RAG reasoning, code generation and math problems. Full benchmarks in this official HF repo. In addition we launch DeepSeek-R1 Qwen 32B Distill, Qwen 32B and Qwen 32B Coder, three very capable models based on the Qwen 2.5 series. Especially Qwen 2.5 32B is a very capable general purpose model for tasks like RAG.

  • DeepSeek-R1: #1 open source reasoning model

    This model beats OpenAI's o1-1217 full o1 model on various tasks like AIME 2024 (math test), SWE-bench Verified (a software engineering task that requires producing GitHub PRs that implement scoped tasks for open source software like Django), and MATH-500. The techniques used in R1 (reinforcement learning on a strong base model) benefit mainly performance on coding, math and logic problems. An incredible release and gift to the community.

    Noteworthy is the addition of several distilled models such as Qwen (1.5B up to 32B) and Llama 3.1 8B and 3.3 70B. Those models seem to benefit greatly from being finetuned (no reinforcement learning) on traces generated by the full R1 model.

    For the model details, the R1 model is a MoE model with 671B parameters and 37B active parameters, 128K context length and basic support for function calling. It being a reasoning model will tend to generate many more output tokens for a given user prompt. The long Chain of Thought is encapsulated in a <think></think> XML tag after which the final answer is provided by the model.

  • Grok-3 released by xAI: #1 in LMSYS leaderboard in all categories

    Impressive release by xAI. They beat o3-mini high, o1, DeepSeek-R1 levels with their latest Grok 3 reasoning models and for some benchmarks beat all of those by a decent margin like in LiveCodeBench (v5) or AIME’25. It was trained on a cluster of 200k H100s, although it's not clear whether all of those GPUs were used for a single run. xAI uses a similar strategy of having both reasoning and non-reasoning models in their latest frontier line-up and have both a big and small model variant. API access is not yet available so benchmark reproductions have not yet happened broadly, although consensus is that this model seems very good.

📦 Repos

  • verl: Volcano Engine Reinforcement Learning for LLMs

    This training stack for large reinforcement learning LLM training by Bytedance is quickly rising in popularity. It's a neat addition to the Hugging Face trainer called TRL.

  • Scaling Retrieval-Based Langauge Models with a Trillion-Token Datastore

    A very cool large scale retrieval system that can perform indexing and querying of a very large scale text corpus. They introduce this system in the context of exploring compute optimal scaling laws (see paper). They state that "such that a smaller model augmented with a large datastore outperforms a larger LM-only model on knowledge-intensive tasks" implying it's more efficient to assume and make available large amounts of data at inference time to dynamically add knowledge instead of using purely pre-training.

    Interestingly, this is aligned with earlier remarks from Wen-mei Hwu (of Programming Massively Parallel Processors book fame) that he shared at a GPU-MODE (kernel programming community) meetup where he said he expects models to "remember" less factual data and retrieve more factual data at inference time. As a path to total system optimization (traditional LLMs have a lot of memory bandwidth pressure because of their size). In a way this is "just RAG" but much more widely than just retrieving a small amount of relevant documents.

  • Verifiers: a set of tools for reinforcement learning with LLMs in verifiable environments

    This is an awesome project showing how to reproduce DeepSeek-R1's proposed method of performing reinforcement learning on strong base models using the GRPO algorithm. The unique value of this repo is on offering an e2e training script to run and allowing you to define reward signals for the GRPO algorithm to use. The author, Will Brown - who is an AI researcher at Morgan Stanley, describes this kind of reward signal creation as rubric engineering. Check out his excellent talk on this idea and play with the code!

    If you want to get good intuition on the GRPO algorithm itself first check out the GRPO explainer video linked in this newsletter's edition.

  • Uncensored DeepSeek-R1 1776 by Perplexity

    They say they've "post-trained it to remove Chinese Communist Party censorship". A useful contribution for everyone that wants their AI features powered by DeepSeek to be a little more Western and a little less CCP. This might have implications for how the CCP sees model drops by Chinese companies like the High-Flyer hedge fund backed DeepSeek. Hopefully there's a limited chilling effect as Chinese teams are great at innovating in this space. The team from Perplexity carefully analyzes benchmark scores in order to avoid performance degradation as a result of the uncensoring. It looks like they've been able to maintain full model performance.

  • s1: Simple test-time scaling

    A simple baseline reproduction of o1-like scaling by performing strictly SFT (not RL tuning) on a strong instruct model (they use Qwen 2.5 32B). They show that a tiny amount of high quality curated reasoning data (1K samples) can make a big difference.

📄 Papers

  • Optimizing Large Language Model Training Using FP4 Quantization

    Frontier teams, in this case Microsoft, continue to push the efficiency of model training. In this paper they show how to mitigate the quantization errors that appear in naive FP4 LLM training. They show BF16 and FP8 levels of quality in experiments at the 100B token/13B parameter scale.

  • Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

    So far, most test-time compute implementations scaled the inference in "token space" where they keep generating tokens in the final output stream to produce long Chain-of-Thought streams in order to improve final answer accuracy. This approach of reasoning in latent space deeper within the model architecture "can capture types of reasoning that are not easily represented in words". They show that their 3.5B model can reach quality of 50B sized models on certain problem cases.

    If you've heard of the 🔄 shape rotaters 🔄 > 🗣️ wordcels 🗣️ meme then you'll probably be glad to hear that shape rotating can sometimes indeed be the winning approach.

  • Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

    DeepSeek releases an inference technique that utilizes train-time algorithms to improve long-context inference performance. They mention scaling up to 64K context window lengths in the paper, the DeepSeek-R1 model goes up to 128K context window but the official DeepSeek inference API goes up to 64K. Most likely, because they're using NSA to improve the efficiency with which they host the model. Very kind of them to share this innovation so openly to help others host DeepSeek better 🙌

  • Training Software Engineering Agents and Verifiers with SWE-Gym

    RL gyms are hot again! They were back in 2016 (check out this throwback banger by OpenAI). This time because various labs (OpenAI, DeepSeek) have found that performing RL on a very strong base LLM (crucial) with verifiable rewards (gyms) actually works.

📚 Resources

  • The Ultra-Scale Playbook: Training LLMs on GPU Clusters

    An incredibly detailed guide from the kind folks at Hugging Face explaining the details of large scale distributed LLM training. They go in-depth on topics like data parallelism (ZeRO-1|2|3) and advanced topics like FP8 training, Flash Attention other kernel optimizations. Great gift to the community. Now all you need is to be GPU rich 🤓

  • NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions

    Meta open sources a large reasoning dataset that they've constructed purely by searching in pre-training data for reasoning traces and by generating relevant matching questions/prompts. Their full data pipeline is LLM based and uses their latest Llama 3.3 70B Instruct model.

  • AgentStack: start your agent project

    A neat scaffolding tool to build agents, it supports the CrewAI, LangGraph, OpenAI Swarms and LlamaIndex frameworks in addition to multiple LLM, tool and observability providers (although as it's a project from AgentOps it biases towards this provider for the observability part).

  • Tool Use, Unified by Hugging Face

    Need write-up of tool use formatting by Matthew Carrigan from Hugging Face. The post explains how tool definitions and tool call signatures are standardized but that more work is required on parsing tool call formats. Overall, tool use is still a complex formatting issue especially for advanced features like constrained decoding and streaming. Luckily my work at Groq aims to make this problem go away magically when you use tools with Groq inference APIs.

  • Model Context Protocol continues to grow: a talk about what's next by Anthropic

    A talk should go up later and I will share it once it does. The TLDR is that MCP seems to be the bet by Anthropic on how agent protocols will take shape and more advanced MCP use cases like making MCP servers both client/server and remote MCP servers, complex authentication support are coming soon. I'd recommend checking the MCP docs every now and then as it's developing quickly.

    I think going into why MCP could be the basis of agents warrants an entire separate blog post but the TLDR is that MCP makes it easy to delegate tool execution such that an agent (client program) doesn't need to implement using the tools (e.g. a tool call to fetch Linear tickets can now be delegated by an MCP server that the actual Linear team maintains).

    It also allows for complex delegation patterns (sub-agents) where an MCP server handling a tool call itself delegates to other MCP servers and/or makes use of LLM inference in order to satisfy the tool call. This leads to tool calls themselves becoming more "high-level" instead of pure code-style function calling (e.g. fetch JSON of weather data of city X) but more like "fetch my Uber's order status" (perhaps using a delegate browser agent implemented as an MCP server). I like to think of MCP servers as a "GraphQL" like layer that is optimized on the frontend (what the LLM gets) for natural language, but in the backend can call regular REST APIs to perform a requested tool call.

  • General Reasoning releases large scale reasoning dataset

    123k outputs from popular reasoning models like R1, R1-Zero, LIMO, DeepHermes, OpenThoughts, R1-Distil-L70B, DeepScaleR. They have comparison answers from o3-mini and gemini-flash-thinking.

  • [GRPO Explained] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

    Excellent Yannic Kilcher video, as always, about GRPO. He makes the RL algorithm, which is a derivative of PPO (invented by OpenAI co-founder John Schulman) surprisingly easy to understand.

  • Jeff Dean & Noam Shazeer – 25 years at Google: from PageRank to AGI

    Two OG Googlers discuss AGI and the technologies to get there. Fascinating listen.

  • SuperGPQA: large scale GPQA-like benchmark

    The numbers: 26,529 questions across 13 disciplines, 72 fields, and 285 graduate-level disciplines. Some top model rankings: DeepSeek-R1 (62) > o3-mini (high reasoning effort) (55) >= Doubao-1.5-Pro (55) > Claude-3.5-Sonnet (48).

Want more? Follow me on X! @ricklamers

]]><![CDATA[DeepSeek-V3: the model everyone is talking about]]>https://codingwithintelligence.com/p/deepseek-v3-the-model-everyone-ishttps://codingwithintelligence.com/p/deepseek-v3-the-model-everyone-isThu, 02 Jan 2025 13:54:55 GMTDear readers,

Happy New Year and welcome to 2025! This week’s edition is a collection of everything that happened in the final 2-weeks (51/52) of 2024, and BOY did it get busy during that final sprint of the year. If nothing else, I think it signals that 2025 is going to be an incredible year for AI. With democratization of frontier performance (DeepSeek-V3, QwQ, QVQ, Llama 3.3 70B, Qwen 2.5 72B), an incredible installed base of compute clusters (multiple interconnected 100k accelerator clusters, 1M clusters in the works), and new frontier heights (o3) that fully automate most run of the mill software engineering (71.7% on SWE-bench verified), the pace of progress is bound to be electric. Strap in and enjoy the ride!

- Rick Lamers

DeepSeek-V3 Perf/Cost chart: a new position on the Pareto front

📰 News

  • DeepSeek-V3: GPT-4o level open source model

    671B MoE parameters, 37B active, pre-trained on 14.8T tokens. See Technical Report for more details. The takeaway here is that this launch is _drastically_ commoditizing frontier level models. It is significantly cheaper than GPT-4o (at 9:1 input output token it is 1/9th the cost). It claims to rival Claude Sonnet 3.5 but many evals (e.g. SWE-bench verified) still show Sonnet 3.5 (v2) beating it slightly. Training cost was allegedly around $5.5M (but maybe even cheaper because that's calculated at hourly prices of renting H800s).

  • QwQ: 32B reasoning model

    o1-mini level performance. First truly open source reasoning model. Read the blog post for details. Apache 2.0 licensed.

  • QVQ: 72B vision reasoning model

    Gets close to latest o1 (o1 2024-12-17) vision performance as measured by vision evals like MathVista. Uses 'qwen' license.

  • o3 "cracks" ARC-AGI-1

    o3 does well on ARC-AGI-1 which many predicted would take a long time. As is tradition in machine learning, once a task has been mastered the goal post is quickly moved, and we're left searching for the next task that isn't solved. The AGI definition of MSFT for OpenAI of "excess of $100B in profits" seems most robust as humans are pretty good at trying to compete away profits by attacking high margin activities.

    I'm looking forward to the next eval folks are targeting. It might be ARC-AGI-2 (on which o3 apparently gets 30%) or Frontier Math by Epoch AI (on which o3 gets about 25% atm). The more evals start to resemble utility in terms of useful for societal (economic) operating activity, the more the artifacts that beat them actually end up making a difference in practice (getting 71.7% on SWE-bench verified like o3 does means we can all automate a significant portion of software engineering work).

    Folks have pointed out that o3-mini will be the more cost effective option (beating full o1 at several tasks) but since neither o3 or o3-mini are being made available not much attention has gone to it.

  • Groq Appgen: instant web app generation on Groq

    I've personally built this application 😎 (with contributions from my awesome colleagues Jose Menendez and Benjamin Klieger) showcasing the power of Groq speed (speculative decoding enabled achieves 2k tokens per second on 70B!) with the Llama 3.3 70B model for strong code generation capabilities. Check out this X video by my colleague Benjamin: https://x.com/benklieger/status/1870277109601771851

    We've also open sourced the entire implementation https://github.com/groq/groq-appgen

  • Epoch AI: Frontier models have likely gotten much smaller

    Nice investigative work to show that frontier models are becoming smaller. We've long past the stage of a simple "1T models won't ever go into production". Efficiency = margin in the age of scaling AI to broad based use so the investment incentive to make models more efficient is enormous.

  • ModernBERT: a better BERT by Answer.AI

    Good base model for finetuning your task specific embedding model, here's a list of finetunes folks created https://huggingface.co/models?other=base_model:finetune:answerdotai%2FModernBERT-base&sort=downloads

  • YouTube already licensing video data to model companies?

    Haven't seen coverage of this. Neat find by @bilaltwovec.

  • Google DeepMind previews Gemini 2.0 Flash Thinking model

    Noam Shazeer, original author of Attention Is All You Need has returned to Google and posted about this new model on X https://x.com/NoamShazeer/status/1869789881637200228

  • Rare DeepSeek CEO interview

    Read my full thoughts on the interview in this X 🧵 https://x.com/RickLamers/status/1874778471907344825

  • Google DeepMind announces Veo 2

    Impressive video generation model by Google DeepMind. Expectations are generally that Google has a compute (TPUs) and data (YouTube) advantage and will really nail video generation. Will be interesting to see how they go from demo → paid product (APIs on gcloud? YT creator features?).

  • Nebius Cloud team contributes SWE-bench agent using strictly open source models

    Impressive 40.6% resolved rate on SWE-bench Verified. They use Qwen-2.5-72B Instruct and Llama 3.1 70B Base.

  • o1 models available through API

    Interestingly they come with a control parameter called "reasoning_effort" that can be set to "low", "medium" and "high".

📦 Repos

📄 Papers

  • Apollo: An Exploration of Video Understanding in Large Multimodal Models

    Meta researchers explore video understanding with VLMs. Strong VLMs for video understanding could unlock another large amount of pre-training data by "transcribing" videos made in the real-world. Video recording is cheap (smartphones), visual understanding based transcription could yield important real-time/at scale data.

  • Mind the Time: Temporally-Controlled Multi-Event Video Generation

    Interesting new video generation controlling technique.

  • 1.58-bit FLUX

    Bringing low-bit representations to image generation models. Very impressive efficiency gains by folks from ByteDance/POSTECH (SK research university).

  • Memory Layers at Scale

    "On downstream tasks, language models augmented with our improved memory layer outperform dense models with more than twice the computation budget, as well as mixture-of-expert models when matched for both compute and parameters." Meta continuing to advance architecture ideas, and kindly running experiments at scale to aid the GPU poor. Thank you team & Zuck!

🛠️ Products

  • Gemini Deep Research

    Agentic product feature of Gemini Advanced, tapping Google's strong long context performance of their Gemini models. Think of it as LLM+web search on steroids. Neat launch!

📚 Resources

Want more? Follow me on X! @ricklamers

]]><![CDATA[Gemini 2.0: is Google finally where everyone expected it to be?]]>https://codingwithintelligence.com/p/gemini-20-is-google-finally-wherehttps://codingwithintelligence.com/p/gemini-20-is-google-finally-whereSun, 15 Dec 2024 18:42:45 GMTThis week covers 41 (!) updates about what’s happening in AI: spoiler - a lot. I’m sorry I had to skip y’all for a week but it just was so busy at Groq (we shipped Llama 3.3 70B + specdec version - this runs it at over 2000 tok/s on certain queries, check it out!). Enjoy this week’s update ✌️

📰 News

  • DeepMind releases Genie 2: a foundational world model

    Similar to GameNGen by Google Research earlier and Oasis by Decart. It allows a user to provide inputs to have real-time control over a world simulation engine. It seems to suffer from similar issues of earlier attempts like persistence. "we believe Genie 2 is the path to solving a structural problem of training embodied agents safely while achieving the breadth and generality required to progress towards AGI". You can be sure that teams like the folks behind the Tesla Optimus humanoid robot are experimenting with these world simulation techniques.

  • Gemini 2.0

    Google really outdid themselves with this release, not only does the model score exceptionally well on coding benchmarks versus Claude Sonnet 3.5 (new) but also does it ship with advanced agentic features like realtime voice + video input modes in the Live API. Google is now close to or depending on your POV leading in the AI race and it’s what people expected from the company since the open sourcing of TensorFlow. Especially notable is that they’re still completely unbeaten on video input and long context (up to 10M and 2M in prod).

  • OpenAI announces realtime video mode

    Similar to Gemini Flash 2.0 realtime mode. Only Google got there first this time :) When access rolls out more broadly evals will likely show which of these does best (under which conditions). Tracking!

  • ChatGPT Pro Mode: o1 pro mode

    o1 pro mode scores high on reasoning tasks like PhD-Level Science Questions (GPQA Diamond), Competition Code (Codeforces), Competition Math (AIME 2024). It's pretty slow with a loading bar and has been described as "integrated best-of-n sampling" giving you sort of a pass@k level performance where k is something like 5 versus regular o1. Not sure if it warrants the $200 price tag associated to ChatGPT Pro Mode. Others have pointed out that $200 is maybe worth it for the increased o1 (non pro) rate limit increase. I guess it will be TBD how much traction the high price plan for ChatGPT will get.

  • DeepSeek releases update V2.5: 1210

    Improved math & coding ability wrt original V2.5: MATH-500 benchmark increased from 74.8% to 82.8%. LiveCodebench (08.01 - 12.01) benchmark increased from 29.2% to 34.38%.

  • Microsoft releases Phi-4: 14B math buff

    "Phi-4 outperforms much larger models, including Gemini Pro 1.5, on math competition problems". The technical report has been lauded for digging into the synthetic data generation strategies used. https://techcommunity.microsoft.com/blog/aiplatformblog/introducing-phi-4-microsoft%E2%80%99s-newest-small-language-model-specializing-in-comple/4357090 and unofficially on HF https://huggingface.co/matteogeniaccio/phi-4.

  • Snowflake releases strong permissively licensed multi-lingual embedding models: Arctic Embed 2.0

    Very useful! Around the level of text-embedding-3 from OpenAI at 1/3rd of the dimension size.

  • Amazon releases Nova model series

    Check out the technical report here https://www.amazon.science/publications/the-amazon-nova-family-of-models-technical-report-and-model-card. Their best model is approximately Llama 3.2 90B/Llama 3.1 70B level but Nova isn't open source. The report also highlights image generation and video generation models, showing that these models are presumably becoming table stakes technologies for large tech companies like Amazon/Google/Tencent/Alibaba/Meta etc.

  • $1M prize for beating a new SWE-bench like benchmark

    Unreasonably high requirement (90%? open source only?). But interesting initiative to track SWE agent progress regardless.

  • DeepSeek-VL2: MoE A4.5B vision model competitive with Qwen 2-VL 7B/Pixtral 12B

    Meaningful performance above Pixtral 12B at a much lower active param budget. Quite impressive.

  • Grok image generation model Aurora released

    Grok 2 initially relied on FLUX for image generation on the X platform but now the team seems to be getting their image gen muscle on. Images are decent but not yet surpassing SOTA like Ideogram or FLUX1.1 [pro]. No API release as of yet.

  • Nous DisTrO: distributed model training

    Nous has been making nice progress with distributed model training, one of the neat results is the DeMO paper which was co-authored with Diederik P. Kingma the author of the original Adam optimizer. DeMO shows controlled divergence can enable large scale distributed training. The Nous model doesn't have great MMLU scores that can compete with the slew of open source models available (all trained on fast interconnect clusters of course), but it's encouraging to see work in this area to guarantee we'll be less dependent on centralized training initiatives if we need to be in the future. In essence, this line of work is a nice hedge on centralization power. Which likely helps the centralized initiatives behave better as a result ;-)

  • Fish Audio 1.5 release: SOTA open source TTS

    The gap between proprietary (ElevenLabs) and open source is shrinking. Try the playground at https://fish.audio/ it's very impressive. Check out the ranking of TTS models in the helpful HF leaderboard https://huggingface.co/spaces/TTS-AGI/TTS-Arena

📦 Repos

📄 Papers

  • Adam-mini: Use Fewer Learning Rates To Gain More

    Reduced memory footprint version of the successful Adam optimizer. Awesome work for hobbyist fine-tuning that typically try to squeeze the most out of their hardware. Support seems to have already landed in LLaMA-Factory https://github.com/hiyouga/LLaMA-Factory

  • 🦣 MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale

    "Using only open models, we create a dataset containing 12M instruction-response pairs to cover diverse, reasoning-intensive tasks with detailed and faithful rationales." nice study that shows that post-training for reasoning in the vision domain has lots of potential. They set SOTA scores on vision reasoning tasks like MMMU-Pro (+7%).

  • Byte Latent Transformer: Patches Scale Better Than Tokens

    Meta frees us from the burden of tokenizers, at last. "We present the first flop controlled scaling study of byte-level models up to 8B parameters and 4T training bytes." and "Patches are segmented based on the entropy of the next byte, allocating more compute and model capacity where increased data complexity demands it." Meta truly GOAT, this is phenomenal.

  • Attamba: Attending To Multi-Token States

    Compressing tokens to avoid "quadratic scaling of compute with sequence length", clever approach and quality hit seems minimal.

  • O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?

    They explore finetuning on O1 API data (against ToS, but hey, science!) and remark on the importance of not relying (solely) on distillation for achieving SOTA on open models.

  • Training Large Language Models to Reason in a Continuous Latent Space

    "By delaying definite decisions and expanding the latent reasoning process, the model pushes its exploration closer to the search tree’s terminal states, making it easier to distinguish correct nodes from incorrect ones."

  • Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?

    Of course the answer is yes, but the paper highlights in a helpful way that it's only the case for certain types of queries. Shortcut exploitation is a notorious issue with machine learning models and undermines generalization ability of models.

  • PaliGemma 2: A Family of Versatile VLMs for Transfer

    They highlight how their models can be used for transfer-learning to domain specific tasks like "table structure recognition, molecular structure recognition, music score recognition, as well as long fine-grained captioning and radiography report generation".

  • JetFormer: An Autoregressive Generative Model of Raw Images and Text

    "... most of these models still rely on many separately trained components such as modality-specific encoders and decoders. In this work, we further streamline joint generative modeling of images and text. We propose an autoregressive decoder-only transformer - JetFormer - which is trained to directly maximize the likelihood of raw data, without relying on any separately pretrained components, and can understand and generate both text and images." I like approaches that are aligned with the Bitter Lesson https://www.cs.utexas.edu/~eunsol/courses/data/bitter_lesson.pdf

  • Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling

    This training-free sampling guidance method meaningfully boosts the quality of the generated videos. Magical.

  • The broader spectrum of in-context learning

    This paper by folks from Google DeepMind places the phenomenon of in-context learning in a broader context of research on generalization and meta learning. Helpful if you're interested in learning more about in-context learning and how it will develop over the next years of AI research.

🛠️ Products

  • GitHub Copilot on the web

    GitHub now has a chat feature that supposedly integrates with GitHub data (through GitHub APIs) well. My test queries didn't fare very well, let me know in the comments if you have more success!

  • Luma Labs releases image generation model Photon

    Competitive with other SOTA. Not open source. Stunning images, but fails my "a coffee cup upside down on a table" prompt adherence test. Oh well, more work to be done I guess :)

  • Repo Prompt: codebase to prompt

    Utility to generate prompts from your codebase. Handy for models like O1 Pro Mode that don't have APIs yet. A handy crutch for the gap between those models having APIs (at which point the Repo Prompt utilities will just be handled by Cursor/Windsurf AI-first coding IDEs).

  • Serper: Google SERP search for LLMs

    Came across this while looking at the Arena Agent leaderboard (cool project by Gorilla, see link in this newsletter). https://www.agent-arena.com/leaderboard

📚 Resources

Want more? Follow me on X! @ricklamers

]]><![CDATA[Model Context Protocol: making LLMs more useful]]>https://codingwithintelligence.com/p/model-context-protocol-making-llmshttps://codingwithintelligence.com/p/model-context-protocol-making-llmsSun, 01 Dec 2024 20:18:33 GMT
Model Context Protocol architecture

📰 News

  • Anthropic launches Model Context Protocol

    "open protocol that enables seamless integration between LLM applications and external data sources and tools". It uses JSON messages to allow for communication between:

    Hosts: LLM applications that initiate connections
    Clients: Connectors within the host application
    Servers: Services that provide context and capabilities

    Using MCP folks will be able to use LLMs more directly with the applications they use every day. Because the protocol is open it requires less coordination between walled gardens of your data silos. Anthropic probably identified that the value will be much greater to let this proliferate as an open standard versus making this something proprietary that gets little adoption. Does HTTP ring a bell? I think this is a launch to follow. Start playing with the awesome-list MCP servers linked in this post!

  • Ai2 releases OLMo 2: the awaited SOTA truly Open Source LLM

    They announce a model competitive with the Llama 3.1 8B model with a truly open approach (they open source pretraining code, data, weights, under permissive licenses, etc.). Truly a gift to the community.

  • SmolVLM - small yet mighty Vision Language Model

    Not better than Qwen2-VL 2B but beats moondream2 and PaliGemma 3B. Impressive contribution by the HF team for small footprint VLMs. The efficiency can make this interesting for large scale data processing too like data extraction.

  • AI Agent prompt hacking competition wins developer $50k

    Neat concept: an AI agent controls an account with an initial $3k balance. You can attempt to prompt-convince it to wire you the money (over crypto) through its function calling abilities (approveTransfer). It's instructed however to not wire any more. The anonymous developer took home 30% of the prize as a fee so that was a quick way to earn $14.1k :)

📦 Repos

  • Mochi 1 LoRA Fine-tuner

    Neat single script video language model fine-tuning script that can run on a single H100/A100.

  • Awesome MCP Servers

    MCP is getting adopted fast, a sprawling collection of projects making models more useful by glueing services through the Model Context Protocol. "@modelcontextprotocol/server-filesystem 📇 🏠 - Direct local file system access." some are pretty tricky if used maliciously! Use with caution. And of course, if you build any cool MCP servers put them on the list with a PR!

  • voice-pro: zero-shot Voice Cloning & more

    Controversial topic with deep fake attacks running rampant, but I think it's good to democratize the technology and make everyone robust against the existence of this technology. In addition, it serves as a great learning tool and a cost reducer for folks who have a genuine use case (automate your tutorial recordings :)?).

  • steel-browser: a tool for building browser/agent automations

    Very cool concept, AI-powered Selenium/Playwright is taking flight.

  • Yet Another Agent Framework (YAAF): Multi-Agent Orchestrator by AWS

    It's not called YAAF I made that up, but at this point I wonder how many more agent frameworks we need :)

  • Srcbook: AI-first app development platform

    Forget about mobile first, AI-first app development platforms lean into strengths/weaknesses of AI code gen limits to facilitate building apps quickly. Neat idea! Kind of the natural expansion of Claude Artifacts?

📄 Papers

  • Large Multi-modal Models Can Interpret Features in Large Multi-modal Models

    Mechanistic Interpretability work can be very tedious, the LLMs-Lab Team from NTU did what any reasonable AI-loving engineer/researcher would do: use AI. They've repurposed VLMs (like LLaVA-OV-72B) to identify features to be used for model steering. Looks a lot better than scaling manual approaches that are prominent in MI work to date.

  • Star Attention: Efficient LLM Inference over Long Sequences

    A contribution from NVIDIA to reduce inference time 10x with minimal quality loss (95-100%). An approximation allows for parallel and low-communication overhead calculation of attention. There's also a repo: https://github.com/NVIDIA/Star-Attention

  • Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics

    “our experimental results across several LLMs show that LLMs perform arithmetic using neither robust algorithms nor memorization; rather, they rely on a "bag of heuristics" -> really shows that circuitry formation inside LLMs leaves a lot to be desired, will be an important branch of research for frontier labs.

📱 Demos

📚 Resources

  • An Intuitive Explanation of Sparse Autoencoders for LLM Interpretability

    Good starter post if you want to learn about techniques used for creating feature steering techniques like “Golden Gate Claude”.

  • AI Benchmarking Hub by Epoch AI

    Epoch AI has been dropping resources for keeping up with progress in the field of AI and this benchmark database is a welcome addition to see SOTA results at a glance. Ty guys!

  • MCP cost/speed/intelligence trifecta

    This was a neat find by the https://buttondown.com/ainews newsletter. MCP has a built-in way to set preferences for cost, speed and intelligence. Likely the inputs to a model router system that will handoff to the right model automatically based on the request.

  • Quantization-Aware Training for Large Language Models with PyTorch

    There is more evidence (https://arxiv.org/abs/2411.04330) building that precision during training influences post-training inference optimization techniques like quantization. Therefore, it's great to see PyTorch prioritizing making techniques like quantization-aware training more accessible. Especially for more broadly performed activities beyond frontier labs like fine-tuning.

  • Model Context Protocol (MCP) Quickstart

    Neat write-up on Anthropic's newly launched Model Context Protocol (MCP) to get the TLDR.

Want more? Follow me on X! @ricklamers

]]><![CDATA[Open Source o1 has (almost) arrived: DeepSeek R1-Lite-Preview]]>https://codingwithintelligence.com/p/open-source-o1-has-almost-arrivedhttps://codingwithintelligence.com/p/open-source-o1-has-almost-arrivedSun, 24 Nov 2024 21:46:23 GMT

📰 News

  • DeepSeek-R1-Lite-Preview: o1-preview-level performance on AIME & MATH benchmarks

    Awesome to see that moats aren't very long lived. DeepSeek promised to open source this model too, hasn't dropped yet but I'm sure the community is anxiously waiting.

  • XGrammar: new Structured Generation library by the MLC project

    The repo: https://github.com/mlc-ai/xgrammar

  • step-2-16k-202411: a mysterious 1T model just appeared

    Livebench.ai is a neat benchmarking attempt that constantly updates to avoid being saturated by Training On the Test Set. step-2-16k-202411 ranked just below o1-mini and above last week's entrant gemini-exp-1114. Although 1T seems prohibitively expensive to run. Reportedly Claude Opus 3.5 isn't being deployed for the exact reason of the economics of the scale of the model not weighing up against the performance delta versus other (smaller models) like Claude Sonnet 3.5.

  • Pixtral Large 124B

    It performs well, from the blog: outperforming all of Claude-3.5 Sonnet (new). Unfortunately, only available under non-commercial research license. Great work though from the folks at Mistral, and kudos for an open weights release!

  • Qwen2.5-Turbo: 1M long-context Qwen

    Impressive performance and evals (NIAH, RULER and LV-Eval). Congrats team Qwen! It's not released as a model/inference setup though, API only.

  • Marco-o1: open source o1?

    CoT fine-tuning, MCTS for search. First impression this doesn't look very good. At least on the small model they performed these steps on it's only marginally better than Qwen 2 7B.

  • Want to also make one remark about Google DeepMind folks dropping another model, the gemini-exp-1121 model, which purportedly is even better than gemini-exp-1114, YMMV but whatever the case, I’m glad labs are making improved models available more quickly to end users!

📦 Repos

📄 Papers

  • BALROG: Benchmarking Agentic LLM/VLM Reasoning On Games

    The Return of Games for benchmarking. Pre-ChatGPT/GPT-3 using games to benchmark AI was all the rage with Lee Sedol's famous Go match and OpenAI beating pro humans in Dota 2 and of course the famous Atari benchmarks. We'll see how things go when using games to evaluate LLMs. Signal or noise? The current leaderboard seems to decently rank models although in the top ranks it's not clear whether the ranking is really definitive (Llama 3.1 < GPT-4o-mini? Llama-3.1-70B-it > Llama-3.2-90B-it?).

  • What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?

    "begin to copy the exact reasoning steps from the training set" a useful observation that can lead to higher quality pre-training by focusing on including general reasoning patterns in the pre-training corpus. Or even steering towards these in post-training to eliminate flawed non-general reasoning patterns.

  • DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging

    "improves the perplexity of the model without increasing its size ... an additional averaging step after each transformer block ... coherent patterns of information flow, revealing the strong and structured reuse of activations from distant layers" looks like carrying information forward is important. Will be interesting to see if this "architecture trick" is adopted by open source model providers. Of course, proprietary labs might already know of this and use it (or similar) in their models.

📱 Demos

🛠️ Products

  • Morph: Infinibranch

    Interesting idea of branching agents through snapshotted VMs. Potentially powerful in the new search oriented inference-time compute scaling that labs are exploring. Private preview unfortunately it seems.

📚 Resources

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]]><![CDATA[Gemini Exp 1114: overfitted to benchmarks or new king?]]>https://codingwithintelligence.com/p/gemini-exp-1114-overfitted-to-benchmarkshttps://codingwithintelligence.com/p/gemini-exp-1114-overfitted-to-benchmarksSun, 17 Nov 2024 22:59:09 GMT
Exp 1114 = Gemini 2?

📰 News

📦 Repos

📄 Papers

  • The Surprising Effectiveness of Test-Time Training for Abstract Reasoning

    Test-Time for the ARC challenge. Interesting ideas by a team from MIT.

  • BitNet a4.8: 4-bit Activations for 1-bit LLMs

    Adaptive quantization and efficient low-precision implementations can push the needle on efficiency. Paired with the Scaling Laws for Precision paper this presents an interesting push on the frontier of efficient AI systems.

  • Needle Threading: Can LLMs Follow Threads Through Near-Million-Scale Haystacks?

    TLDR "Strikingly, we find that many models are remarkably thread-safe: capable of simultaneously following multiple threads without significant loss in performance. Still, for many models, we find the effective context limit is significantly shorter than the supported context length, with accuracy decreasing as the context window grows."

  • Scaling Laws for Precision

    "For inference, we find that the degradation introduced by post-training quantization increases as models are trained on more data, eventually making additional pretraining data actively harmful. For training, our scaling laws allow us to predict the loss of a model with different parts in different precisions, and suggest that training larger models in lower precision may be compute optimal."

📱 Demos

🛠️ Products

📚 Resources

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]]><![CDATA[Chinese labs drop some incredible models]]>https://codingwithintelligence.com/p/chinese-labs-drop-some-incrediblehttps://codingwithintelligence.com/p/chinese-labs-drop-some-incredibleSun, 10 Nov 2024 22:25:30 GMT
The 389B A52B Instruct model performance

📰 News

📦 Repos

📄 Papers

📱 Demos

  • Vision Language Models are In-Context Value Learners

    Try the Interactive Illustration, it impressively demonstrates how complementary robotics and vision language models are. Expect acceleration of Robotics especially in control like bimanual manipulation tasks. By DeepMind, UPenn, and Stanford.

📚 Resources

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]]><![CDATA[Full World simulation just had its ImageNet moment]]>https://codingwithintelligence.com/p/full-world-simulation-just-had-itshttps://codingwithintelligence.com/p/full-world-simulation-just-had-itsSun, 03 Nov 2024 19:57:41 GMT

I don’t believe folks are grasping the implications of this achievement yet. But the ability to simulate full world environments at increasingly higher levels of fidelity will usher in an era of robotics and real-world reasoning the consequences of which are hard to fully comprehend. Incredible work from the folks at Decart.

📰 News

📦 Repos

📄 Papers

📱 Demos

📚 Resources

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]]><![CDATA[Claude Computer Use: RPA on steroids]]>https://codingwithintelligence.com/p/claude-computer-use-rpa-on-steroidshttps://codingwithintelligence.com/p/claude-computer-use-rpa-on-steroidsSun, 27 Oct 2024 15:16:17 GMTWhat a BUSY week! Both for me personally (Sunday newsletter day, yay!) and in AI at large. I think everyone saw the Claude Computer Use release and tried answering the question: how ready is this? See this week’s resources for a hint of the risks currently involved and play with it on your device through Agent.exe - proceed with caution!

📰 News

📦 Repos

📄 Papers

📱 Demos

📚 Resources

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]]><![CDATA[Edge AI makes waves: Qwen 2.5 Code Interpreter in your browser]]>https://codingwithintelligence.com/p/edge-ai-makes-waves-qwen-25-codehttps://codingwithintelligence.com/p/edge-ai-makes-waves-qwen-25-codeSun, 20 Oct 2024 19:28:48 GMT

Today’s demo is the most awesome Edge AI application I've seen, check it out. Python generated & executed fully in your browser.

📱 Demos

📰 News

📦 Repos

📄 Papers

📚 Resources

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]]><![CDATA[Aria MoE A3.9B a new open source multimodal LLM]]>https://codingwithintelligence.com/p/aria-moe-a39b-a-new-open-source-multimodalhttps://codingwithintelligence.com/p/aria-moe-a39b-a-new-open-source-multimodalSun, 13 Oct 2024 13:39:11 GMT

📰 News

📦 Repos

📄 Papers

📱 Demos

📚 Resources

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]]><![CDATA[A new kind of Foundation Models: LFMs]]>https://codingwithintelligence.com/p/a-new-kind-of-foundation-models-lfmshttps://codingwithintelligence.com/p/a-new-kind-of-foundation-models-lfmsSun, 06 Oct 2024 23:45:35 GMT
Liquid AI’s new 1B, 3B and 40B LFMs

📰 News

📦 Repos

📄 Papers

📱 Demos

📚 Resources

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