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reviewing every PR (for the past couple of months, we’ve been leaning on Codex for this), and then a human engineer makes the final approval. When the pull request is merged, our Github integration marks the related issue in Linear as ‘Done’. Stage 5: Close the loop with a happy customer⁠ When an issue is marked ‘Done,’ every related feature request reopens in Intercom with a note for the CX team to know that the customer’s request is fulfilled. The team then follows up with each one, which Ale

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T-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” and “coding harness” somewhat interchangeably for simplicity. (Strictly speaking, the agent is the model-driven decision-making loop, while the harness is the surrounding software scaffold that provides context, tools, and e

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ts are Trinity Mini (26B with 3B active parameters) and Trinity Nano (6B with 1B active parameters) Along with the model weights, Arcee AI also released a nice technical report on GitHub (as of Feb 18 also on arxiv) with lots of details. So, let’s take a closer look at the 400B flagship model. Figure 2 below compares it to z.AI’s GLM-4.5, which is perhaps the most similar model due to its size with 355B parameters. As we can see in the Trinity and GLM-4.5 comparison, there are several interestin

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nd changes. The agent can take before-and-after screenshots, navigate the app in a real browser, and confirm its work visually—just as a human would. The whole setup is wired into GitHub, Slack, Buildkite, and Ramp's observability stack including Sentry and Datadog. Running everything inside a single sandbox means the agent has the same low-latency access to services, files, and tools that an engineer would have locally. There's no network hop between the agent and the test suite, no remote file

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deas, notes, and papers that did not quite fit into the final chapter narrative but are still worth sharing. I also plan to add more code implementations to the bonus materials on GitHub over time. Table of Contents (Overview) Inference-Time Scaling Overview; Chain-of-Thought Prompting; Self-Consistency; Best-of-N Ranking; Rejection Sampling with a Verifier; Self-Refinement; Search Over Solution Paths; Conclusions, Categories, and Combinations; Bonus: What Do Proprietary LLMs Use? You can use th

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ed to track the leak. But before investing more time, we decided to be sure that this leak was reproducible by others. We reached out to the vLLM team by opening an issue on their GitHub repository , which helped confirm we weren't the only ones seeing this issue and a deeper investigation was warranted. In order to better track what was happening, we turned to Heaptrack : a memory profiler that overrides memory operations like malloc or free and records these events alongside stack traces. Mill

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t-time compute 1487 cycles : Claude Opus 4.5 after 11.5 hours in the harness 1363 cycles : Claude Opus 4.5 in an improved test time compute harness after many hours Download it on GitHub . If you optimize below 1487 cycles, beating Claude's best performance at launch, email us at performance-recruiting@anthropic.com with your code and a resume. Or you can apply through our typical process , which uses our (now) Claude-resistant take-home. We're curious how long it lasts. Product updates, how-tos

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te: does the code run and do the tests pass? Two widely used coding agent benchmarks, SWE-bench Verified and Terminal-Bench , follow this approach. SWE-bench Verified gives agents GitHub issues from popular Python repositories and grades solutions by running the test suite; a solution passes only if it fixes the failing tests without breaking existing ones. LLMs have progressed from 40% to 80% on this eval in just one year. Terminal-Bench takes a different track: it tests end-to-end technical ta

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clusion Building these tools is ridiculously easy, gives you all the freedom you need, and makes you, your agent, and your token usage efficient. You can find the browser tools on GitHub. This general principle can apply to any kind of harness that has some kind of code execution environment. Think outside the MCP box and you'll find that this is much more powerful than the more rigid structure you have to follow with MCP. With great power comes great responsibility though. You will have to come

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where the model’s predicted answer letter is compared directly to the correct one. Two other popular methods exist that involve log-probability scoring. I implemented them here on GitHub. (As this builds on the concepts explained here, I recommended checking this out after completing this article.) The following subsections illustrate how the MMLU scoring shown in figure 2 can be implemented in code. 1.2 Loading the model First, before we can evaluate it on MMLU, we have to load the pre-trained

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utor), Mark Saroufim (PyTorch), Kyle Krannen (NVIDIA, Dynamo), and Ashish Vaswani for reading pre-release version of this blog post and providing feedback! References vLLM https://github.com/vllm-project/vllm; "Attention Is All You Need" https://arxiv.org/abs/1706.03762; "Efficient Memory Management for Large Language Model Serving with PagedAttention" https://arxiv.org/abs/2309.06180; "DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model" https://arxiv.org/abs/2405

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d AI, not AI that leads humans. Conclusion Since the early ChatGPT Plugin days 2+ years ago we know about the risks of automatic tool invocation. From stealing emails, to exposing GitHub repositories and even remote code execution there is a lot that can go wrong. AI Apps and Agents over-rely on model behavior to enforce security. This is often a fundamental design flaw in agentic systems. The model might have a backdoor or hallucinate, and even worse, if there is no attacker in the loop (indire

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ntly reduce both cold (first time) and warm (second time and on) start up for vLLM, especially as related to Dynamo and Inductor compilation. Please follow the startup-ux label on GitHub or join the feat-startup-ux channel on vLLM Slack to stay updated on the progress! An important UX improvement is the planned revamp of the -O command-line flag. By specifying -O on the vLLM CLI (where n is an integer between 0-3), users will get easier direct control over trading off startup time for performanc

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. There are still a few usability concerns in XGrammar v0 integration to match feature parity with all use cases: It is yet to support grammars other than GBNF format (PR on vLLM: github); It is yet to support regex; It is yet to support complex JSON that uses regex patterns or numeric ranges There are a few PR trying to cover this usage. There was one bugfix PR on vLLM and one upstream vLLM now has a basic support for XGrammar by default. In case where we know XGrammar is insufficient to serve

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boundaries of innovation while making it accessible to all. For collaboration requests or inquiries, reach out at vllm-questions@lists.berkeley.edu. Join our growing community on GitHub or connect with us on the vLLM Slack. Together, let’s drive AI innovation forward. Acknowledgments We extend our gratitude to the uv community — particularly Charlie Marsh — for creating a fast, innovative package manager. Special thanks to Kevin Luu (Anyscale), Daniele Trifirò (Red Hat), and Michael Goin (Neura

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Devin independently completing an Upwork bounty, installing and running a PyTorch project without human intervention.1 The company claimed Devin could resolve 13.86% of real-world GitHub issues end-to-end on the SWE-bench benchmark - 3x times better than previous systems. Only a select group of users could access it initially, leading to breathless tweets about how this would revolutionize software development. As a team at Answer.AI that routinely experiments with AI developer tools, something

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ng test results as feedback; The problem space is well-defined and structured; and Output quality can be measured objectively. In our own implementation, agents can now solve real GitHub issues in the SWE-bench Verified benchmark based on the pull request description alone. However, whereas automated testing helps verify functionality, human review remains crucial for ensuring solutions align with broader system requirements. Appendix 2: Prompt engineering your tools No matter which agentic syst

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depending on the GPU memory and compute resource of the GPUs For quick setup, we have compiled the Docker Image of vLLM 0.6.2 (commit: cb3b2b9ba4a95c413a879e30e2b8674187519a93) to Github Container Registry. To get download the image: To launch a docker container with the image run: Now launch the LLM server with the parameters that we have found: Conclusion This guide has explored the power of vLLM for serving large language models on AMD MI300X GPUs. By meticulously tuning key settings like chu

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uction). Consequently, much of the agent demo code focuses on parsing model output Cited as: Weng, Lilian. (Jun 2023). “LLM-powered Autonomous Agents”. Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/. Or [1] Wei et al. “Chain of thought prompting elicits reasoning in large language models.” NeurIPS 2022 [2] Yao et al. “Tree of Thoughts: Dliberate Problem Solving with Large Language Models.” arXiv preprint arXiv:2305.10601 (2023). [3] Liu et al. “Chain of Hindsight Aligns Language M

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