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agent runtime handling everything from the model adapter down to the core execution loop. It speaks Deepseek V4’s native tool semantics instead of routing calls through a generic OpenAI-style interface. For this model, Deepseek also has not ship a standard chat template, so small serialization mismatches can silently drop model performance. During testing on Fireworks, we found that a "minimal-first" setup yields much more consistent tool calls than jumping straight to the full standard catalog

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is without watermarking. We often sample without a random seed because I know most people don’t even use one. I honestly don’t think you can necessarily do it with the Claude and OpenAI APIs. I know you can do it in Ollama, but I also always had some problems with that because I used Ollama in one of my books for the bonus material to generate some texts. I was fixing the random seed, but it still wasn’t always deterministic, and so forth. So it’s tricky. Your mileage may also vary, depending o

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or macOS (Apple Silicon supported for the MuJoCo backend). A Strands-compatible model provider for the agent's reasoning. Amazon Bedrock with AWS credentials, the Anthropic API , OpenAI, or Ollama running locally. Strands Robots with the dataset extras: uv pip install -U "strands-robots[sim-mujoco,lerobot] =0.5.1" . The lerobot extra pulls in LeRobot ( =0.6.1), datasets , av , and torchcodec , so recording and video decode both work without further setup. Refer to installation guide . That's it

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an choose a specific engine or rely on the bundled engines offered by some labs. On OpenRouter, we offer Exa, Parallel, and Perplexity, alongside the native engines from labs like OpenAI, Anthropic, and Google; Search method. Either you can perform the search before calling the model and pass the results in as context, or you can equip the model with a web search tool that it calls at its discretion; Search budget. If you choose the search tool method, you can also give the model a budget for ho

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ide, infosec's a good bet for a long, stable career FOSS NetBSD may have found a new role: The AI-free FOSS OS Increasing awareness and adoption, followed by increasing donations; OpenAI dots inspire open source imitators amid technical difficulties Persistent agents appeal to AI companies, but demand isn't obvious; Ubuntu 'Stonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raski

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it breaks the repository's existing test suite in only 9% of failures. Luna does so in 15%: the GPT-family regression signature, the same 15 to 20% we see across Sol and the other OpenAI-lineage models. Both fail mostly by near miss (DeepSeek 69%, Luna 66%), but the more expensive model is the one more likely to disturb code that already worked. If you deploy Luna, gate it behind a full regression run; DeepSeek needs that guardrail less. Classify the 113 tasks by what the code actually is, and L

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dependency scan output. If AI-assisted changes regress, you can inspect the same production metrics you use for human-authored changes. NIM ships StarCoder2 as a container with an OpenAI-compatible endpoint, which is what most integrated development environment (IDE) assistants expect. Pin the container to a specific version from the NGC catalog, rather than using an unversioned tag. Next, verify the endpoint: No source code is leaving your network at this point. The model endpoint is also the s

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r model serves through the standard embeddings endpoint — same API as the base model, now specialized to your domain: Or call the /v1/embeddings endpoint directly (the response is OpenAI-compatible): Index your corpus once with the same model, and retrieval is a nearest-neighbor lookup over these vectors. What Else We Found Beyond the two headline results, we ran a series of sweeps. The details are out of scope for this post, but the takeaways are worth stating: •Fine-tuning's gain tracks the ba

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lifies bug hunting, but getting good results requires the right prompt, the right scope, and the right number of outcomes per run. For Patch the Planet , our joint initiative with OpenAI to find and fix bugs in open-source software, we pointed Codex at some of the most widely used, heavily audited codebases in the world, like Rust, curl, and zlib. One tool came up again and again in our internal bug-report channels: /goal , which hands Codex an open-ended objective and lets it work independently

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It has been almost two years since OpenAI released o1, a model that popularized the idea of LLM-based reasoning models. DeepSeek-R1 followed about four months later, together with details of a reinforcement learning with verifiable rewards (RLVR) recipe to train such reasoning models. Last week, OpenAI released the GPT-5.6 model family. It comes in three sizes, each with roughly five or six reasoning-effort settings. So yes, reasoning models are here to stay. They have become a standard part of

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content The lab GPT-5.5-Cyber built in a day Reporting discipline is the hard part The moat is gone Lessons learned We’re running Patch the Planet , an ongoing collaboration with OpenAI that pairs Trail of Bits engineers directly with more than 30 open-source projects. Its goal is to front-run a serious problem facing open-source maintainers: highly capable models like GPT-5.5-Cyber will soon create a firehose of bug reports, and OSS maintainers are already spread thin. Our plan is to point Ope

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ihao Liu, Ligeng Zhu, Zijian Zhang, Zenus Zhang, shinan6, DYZhang, Chao Liu, Zhou Yaoyang, gyy0592, AcrossForest, Emin, Qiming Chu, jiaxiaoyu, tastynoob, and zhenwei. 8. References SGLang GitHub Repository; SGLang .claude/skills; SGLang diffusion .claude/skills; AI-Infra-Auto-Driven-SKILLS; AI-Infra-Auto-Driven-SKILLS prompts; Kernel Design Agents (KDA); KernelWiki skill; ncu-report-skill; KDA-Pilot; SGLang Diffusion Advanced Optimizations, LMSYS Blog; OpenAI Codex Prompting: Goal mode; Humanize

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f course, the privacy angle. For example, for organizing and processing my receipts, I’d be more comfortable with a local model ingesting them rather than sending the data over to OpenAI or Anthropic. (Then, if we keep in mind that Anthropic was recently throttling their flagship model’s performance for LLM research, proprietary services may become more restrictive over time, and it’s maybe a good idea to be comfortable with open-weight alternatives as a backup.) And there are many, many additio

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next page, computes dot products in bfloat16 with HMMA, and reduces each page to one block score. ‍ SMG (Serving Model Gateway) is a Rust-based model gateway that sits between the OpenAI-compatible API and the inference engine. Beyond routing and tokenization, SMG takes on a role that is particularly important for multimodal models: it performs all vision preprocessing on the CPU before the request reaches the GPU worker. Image and video inputs need a fair amount of CPU work before they are usef

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blem, largely because the governing equations contain singularities — mathematical points where values approach infinity and traditional models break down. Last fall, a paper used OpenAI’s GPT-5 to find a partial solution for the gravitational energy radiating from cosmic strings, but only for the simplest case of a square loop where the angle α = π/2, or 90 degrees. A unified exact solution — a single, complete mathematical formula that solved the integral perfectly — remained an open problem.

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harness, it could likely perform on par with GPT-5.4 in Codex or Claude Opus 4.6 in Claude Code. That said, some harness-specific post-training is usually beneficial. For example, OpenAI historically maintained separate GPT-5.3 and GPT-5.3-Codex variants. In the next section, I want to go more into the specifics and discuss the core components of a coding harness using my Mini Coding Agent: https://github.com/rasbt/mini-coding-agent. By the way, in this article, I use the terms “coding agent” an

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ns unsolvable problems due to design issues (as discussed in a recent Reddit thread and the new “Why SWE-bench Verified no longer measures frontier coding capabilities“ article by OpenAI). Anyways, back to the topic of MiniMax M2.5 performance. Looking across a broader selection of benchmarks, according to the Artificial Intelligence Index aggregation, GLM-5 remains ahead. This is perhaps no surprise because GLM-5 is still a 4x larger model than M2.5, even though the tokens/sec throughput is qui

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ore compute resources but can give better results. Even in LLM contexts, this idea has been around for a long time. However, I remember it became particularly popular (again) when OpenAI showed an inference-time scaling and training plot in one of their o1 announcement blog articles last year (Learning to Reason with LLMs). I think this figure, adapted from OpenAI’s blog post, nicely captures the idea behind the two knobs we can use to improve LLMs. We can spend more resources during training (m

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rchitecture, using DeepSeek V3 as a base model) helped DeepSeek become one of the most popular open-weight models and a legit alternative to proprietary models such as the ones by OpenAI, Google, xAI, and Anthropic. So, what’s new since V3/R1? I am sure that the DeepSeek team has been super busy this year. However, there hasn’t been a major release in the last 10-11 months since DeepSeek R1. Personally, I think it’s reasonable to go 1 year for a major LLM release since it’s A LOT of work. Howeve

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king the AI to demonstrate it. It has been slightly less than three years since the release of ChatGPT. A few days before that launch, I wrote my first post on this Substack about OpenAI’s earlier GPT-3 model. Then ChatGPT came out, and I wrote immediately afterwards that “I am usually pretty hesitant to make technology predictions, but I think that this is going to change our world much sooner than we expect, and much more drastically. Rather than automating jobs that are repetitive & dangerous

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