#Coding

AICat Wu, owner of the account for Anthropic, shares a retrospective on making Claude Code with the product's early team. Anthropic's linked feature collects accounts from the builders and early users who shaped the tool.

AIAnthropic's Cat Wu asks users to share their top Claude Code use cases, workflows, and artifacts. She highlights a candidate-sourcing workflow in which Claude Code runs a dynamic workflow to find 100 candidates with LinkedIn, Twitter, blog, and podcast links plus a one-line pitch each, then emails the results as an artifact.

AICat Wu says Claude Fable 5 applied propensity score matching to her retention analysis without being asked, comparing users with similar activity levels. She says the model's improved judgment extends across tasks, from writing emails and docs in Cowork to debugging complex errors in Claude Code.

AICognition launched the Devin Security Vulnerability Remediation Program, in which its forward-deployed engineers embed with customer teams to deploy Devin to find, validate, and fix vulnerabilities. The program first works through existing scanner backlogs from tools such as Snyk, SonarQube, and Semgrep, shipping validated fixes as pull requests, then adds Devin Security Swarm for continuous discovery of logic flaws. Most engagements run about six weeks, and eligibility is limited to enterprise Devin Cloud customers meeting the program's requirements.

AICognition has launched Devin Security Swarm, which uses parallel agents to find vulnerabilities across a codebase, confirms exploitability in isolated sandboxes, and opens remediation PRs. In an evaluation on 50 real-world GitHub Security Advisory vulnerabilities, Devin reached 72% recall at about $90.23 per run, compared with 68% for Claude Security at $131.87 per run. The product is available starting today, with scan profiles and incremental scans that process only changed code after the first full baseline.

AIMistral AI released Leanstral 1.5 on Hugging Face as an open-source code agent model for Lean 4 proof assistant tasks. The model uses 119B total parameters with 6.5B activated per token, a 256k context length, and accepts text and image input. The source gives setup paths through Mistral Vibe and a local vLLM server, with recommended settings of temperature 1.0 and reasoning effort set to high for complex prompts. The model is licensed under Apache 2.0.

AIZ.ai has launched ZCode, its official development environment for GLM-5.2, available on macOS, Windows, and Linux. GLM Coding Plan subscribers get 1.5x usage quota in ZCode, and the tool supports bring-your-own-key (BYOK) use with existing subscriptions and APIs.

AIAndrew Ng describes three loops he uses to build 0-to-1 products with AI agents: an agentic coding loop, a developer feedback loop, and an external feedback loop. He says the agentic coding loop runs every few minutes, letting coding agents build, test, and iterate on software for around an hour without human intervention. The developer feedback loop operates over tens of minutes to hours, with humans steering product decisions because they hold a context advantage over AI systems.

AIXiaomi MiMo's account celebrated growing developer adoption of its open-weights models, crediting Cline for building on MiMo. Cline's linked post announced a $9.99/month subscription offering 2-5x discounted access to GLM-5.2 and other open-weight models including DeepSeek, Kimi, MiniMax, MiMo, and Qwen, with a $1.99 promo for sign-ups via npm i -g cline.

AICognition has released a preview of Devin Fusion, a multi-model harness that runs a frontier main agent alongside a cheaper sidekick agent. On FrontierCode 1.1 Extended, the company reports scores near frontier models at up to 60% lower cost per task, and 41% lower cost when paired with Fable 5, which access was suspended from June 12, 2026.

Why it matters: The post explains a sidekick architecture with cached persistent contexts, which contrasts with advisor-style tools and shows how cost cuts depend on the main model's delegation behavior.

AISebastian Raschka's tutorial shows how to build a fully local coding agent by pairing an open-weight LLM served through an inference runtime with an open-source harness that can read files, edit code, and run commands. He recommends Qwen-Code for Qwen3.6, citing Nvidia's Polar paper, which found Qwen models performed best in Qwen-Code. The Qwen3.6 35B-A3B model is about 22 GB to download and needs roughly 30–40 GB of RAM.

AIThe latest Antigravity IDE release includes a new, clearer quota dashboard, as the author previously promised. The post provides no further details on the dashboard's features or metrics.

AICodex can now transfer work threads between a local laptop and a remote host, letting users continue tasks elsewhere after closing the lid. Codex can also orchestrate the handoff automatically. Mark Chen joked that this removes the need to prop the laptop open with physical claws.

AIMckay Wrigley congratulated the Cursor team on more than three years of work and said he is excited to see what they build with compute from SpaceX and xAI. He noted he still keeps the original open-source Cursor repo on his laptop. Background posts from him describe Cursor as his full-time IDE, citing codebase search as a major productivity gain.

AISpaceX has exercised its option to acquire Cursor in an all-stock transaction, aiming to build highly useful AI models. The quoted announcement says SpaceXAI has spent several months jointly training a model with Cursor, to be released soon in Cursor and Grok Build.

AISpaceX has exercised its option to acquire Cursor in an all-stock transaction, with the stated goal of building the world's most useful AI models. Over the past few months, SpaceXAI has been jointly training a model with Cursor, which will be released in Cursor and Grok Build soon.

AIZ.ai has released GLM-5.2, its flagship model for long-horizon tasks, which it says substantially improves on GLM-5.1 and supports a 1M-token context. The model adds IndexShare, which cuts per-token FLOPs by 2.9× at 1M context, and is released under the MIT open-source license.

Why it matters: The source gives benchmark tables against named rival models and deployment settings, useful for judging where GLM-5.2 sits among current flagship models.

AIZ.ai's release notes list GLM-5.2 as supporting 1M lossless context, with improved long-horizon task performance and reduced context drift and goal forgetting. The company says GLM-5.2 achieves open-source SOTA performance on coding and long-horizon task benchmarks. The page also includes the newer GLM-5.3 and GLM-5.3-Flash entries, which are listed above GLM-5.2.

Why it matters: The page lists a dated series of Z.ai model releases, showing how the coding and long-horizon agent line has evolved from GLM-4.5 through GLM-5.2.

AIAmazon CTO Werner Vogels argues that the verification bottleneck matters more than ever. The post is a short reply to Yaron Minsky's remarks on Jane Street's ambition to make formal methods as pervasively useful for building software as sophisticated type systems are today.

AIMoonshot AI published Kimi-K2.7-Code, a coding-focused agentic model built on Kimi K2.6, with a 1T-parameter MoE architecture and 32B activated parameters. The model card reports about 30% fewer thinking tokens than K2.6 and benchmark results against GPT-5.5 and Claude Opus 4.8, with weights and code released under a Modified MIT License.

Why it matters: The model card gives benchmark comparisons against GPT-5.5 and Claude Opus 4.8 on coding and agentic tasks, useful for judging its position among current coding models.

AIThe essay argues that AI compresses the execution layer of software work while decision-making and accountability remain human, so AI is not yet replacing software engineers. It cites AI-attributed layoffs at Block, Snap, and Intuit that the authors say were not driven by AI, and WARN Act filings in which only one company checked an AI box. A Federal Reserve analysis is cited as finding software engineer employment growing about 3 percentage points per year more slowly after ChatGPT than a no-AI counterfactual.

AIFactory is rolling out automated security review in Droid, running a STRIDE-based check on every non-draft PR alongside standard code review. Findings include severity, a CWE reference, an explanation, and a suggested fix, posted as inline diff comments. The feature is available today on all plans, and a deeper multi-agent /security-review deep audit is available for full-repository scans.

AIZed is building DeltaDB, a version control system that records every operation as a fine-grained delta, linking agent conversations to the code they produce. The company says a beta will arrive in a few weeks, and it invites users to join a waitlist. The system is designed so teammates can collaborate on work in progress without waiting for commits, pull requests, or pushes.

AIXiaomi MiMo has released MiMo Code V0.1, an open-source AI coding assistant for the terminal under the MIT license. It ships with MiMo V2.5, a multimodal model offered free for a limited time with a million-token context window. The tool automatically loads existing Claude Code skills, MCP servers and commands, and reuses API configuration, and it supports providers including Anthropic, OpenAI, DeepSeek, Kimi and GLM.

Why it matters: The post specifies MiMo Code's Claude Code compatibility and MIT license, which bear directly on whether existing coding-agent setups can migrate without rework.

AIXiaomi's MiMo team released MiMo Code, an MIT-licensed terminal coding agent built on OpenCode for long-horizon programming tasks. The design centers on three areas: Max Mode parallel sampling that generates five candidates per turn, Goal-based completion verification, and a memory system that checkpoints session state and rebuilds context. The article reports offline benchmark results and a double-blind A/B test with 1,213 pairs in which MiMo Code's win rate exceeded 65% beyond 200 execution steps.

Why it matters: The article explains how MiMo Code handles long-horizon coding through computation, checkpointed memory, and cross-session evolution, useful for judging design tradeoffs in coding agents.

AIEthan Mollick, who had early access to Claude 5 Fable, reports that it outperformed other public models in his tests, including an isochrone travel-time map and a nine-and-a-half-hour software build called Concord. He says the model delegated work to other agents and made many design choices he could not see or weigh in on, leaving him closer to a client than a hands-on operator. He also notes high token usage, frequent fallback to Claude 4.8 Opus under security guardrails, and persistent quirks in its writing style.

AICognition introduced FrontierCode, a coding benchmark built with open-source maintainers that measures whether models produce code a maintainer would merge. On FrontierCode Diamond, the hardest 50 tasks, Claude Opus 4.8 scored 13.4%, GPT-5.5 scored 6.3%, and Gemini 3.1 Pro scored 4.7%. The authors report 81% fewer misclassification errors than SWE-Bench Pro, though this figure comes from their own analysis of agent trajectories.

Why it matters: The benchmark's blocker and rubric design shows how code quality can be measured beyond unit-test correctness, which matters for judging coding agents.

AIXiaomi MiMo has released MiMo-V2.5-Pro-FP4-DFlash, the FP4 backbone behind MiMo-V2.5-Pro-UltraSpeed, with MXFP4 quantization applied only to the MoE experts and a BF16 DFlash drafter for block-diffusion speculative decoding. The backbone has 1.02T total and 42B active parameters, and the drafter proposes blocks of up to 8 tokens per forward pass. The release is supported in SGLang, with example launch commands provided.

AICohere and Cohere Labs released North Mini Code 1.0, an open-weights 30B-A3B mixture-of-experts model for code generation and agentic terminal tasks, under Apache 2.0. The model has 256K context and 64K max output, and is trained for tool use. Its benchmark table lists Terminal-Bench v2 at 36.0, SWE-Bench Verified at 67.6, and LiveCodeBench v6 at 70.3, below Qwen3.6 on several tasks.

Why it matters: The card lists benchmark results against Qwen3.6, Gemma4, and other models, showing where North Mini Code trails on some coding and agentic tasks.

AIEthan Mollick is releasing Co-Existence on October 20, a new book about working with AI systems that are sometimes, but not always, better than humans. The book follows his 2024 title Co-Intelligence, which he says was written about an era of chatbots rather than autonomous agents. Mollick also reports writing every chapter draft himself while using AI readers and fact-checkers, and building the book's website with Claude Code using Opus 4.8.

AIBaidu CoBuddy is now available for free on Novita AI, a code-focused model aimed at developers and AI agents. It offers a 131K context window, up to 65K output tokens, and native tool calling. The model is served through Novita's serverless API for high-throughput, low-latency inference.

AICognition introduced the AI Productivity Guarantee, under which it will issue credits up to $10M if Devin delivers less engineering value than enterprise customers pay for. The company uses an AI estimator to measure hours of productive output, validated against engineers' own estimates of how long the same work would have taken by hand. Value is converted to dollars at a standard global rate and compared against each customer's consumption near the end of the annual contract.

Why it matters: The post explains how Cognition estimates Devin's output in hours and backs the estimate with a $10M credit commitment, a concrete model for measuring AI vendor value.

AICognition built an automated agent that classifies Devin sessions as productive and estimates the human engineering hours each one would have taken. On 233 held-out sessions the estimator reached an rlog of 0.74, with individual errors often 2 to 3 times in either direction but roughly unbiased in aggregate. The system is calibrated to underestimate and is currently running with Devin customers.

Why it matters: The post shows how the measurement design, from hours-based metrics to conservative calibration, determines whether agent productivity estimates can be trusted in aggregate.

AIVarun Mohan announced a new Antigravity IDE release that significantly reduces cases of memory usage blowing up. He said the team is pushing hard on performance and reliability and will address user feedback.

AIEugene Yan says stronger models have made finding software vulnerabilities easier, so the bottleneck has moved to verification, triage, and patching. He shares lessons from working with security teams on these new bottlenecks and links to a Claude blog post on using LLMs to secure source code.

AIGoogle has released a new version of Gemini 3.5 Flash in Antigravity that reportedly shows less drift and greater endurance on harder tasks. The company also reset Gemini rate limits for all users so they can start using the updated model immediately.

AIXiaomi MiMo will credit a free Token Plan of the same tier to users whose plan expired before May 27. Users who renewed after May 27 will receive a balance matching their renewal amount, usable toward API usage, and both are valid for one calendar month.

AIMiniMax has released MiniMax-M3, a native multimodal model with a 1M-token context window, roughly 428B total parameters, and about 23B activated parameters. The model introduces MiniMax Sparse Attention, which the source says delivers 9× prefill and 15× decode speedups over M2 at 1M context. M3 supports enabled, adaptive, and disabled reasoning modes through the thinking parameter, and weights are available on Hugging Face.

Why it matters: The source gives concrete attention-efficiency figures and three reasoning modes, which helps readers judge long-context cost against deployment choices.

AICognition has announced Devin Desktop, the next generation of Windsurf, which makes the Agent Command Center the default IDE surface for managing local and cloud agents, PRs, and context. Spaces let related agents share context, and Agent Client Protocol (ACP) support lets any ACP-compatible agent run alongside Devin. The IDE remains fully backwards-compatible with Windsurf, including editor extensions, keybindings, LSPs, and terminal workflows.

AIMiniMax released M3, an open-weight model with a 1M-token context window, native image and video input, and desktop operation support. The post credits a new sparse attention architecture, MSA, for long-context gains, reporting over 9x prefilling and over 15x decoding speedups and 59.0% on SWE-Bench Pro. The API and MiniMax Code are available now, with the technical report and open weights promised within 10 days.

Why it matters: The post pairs a new sparse attention design with benchmark figures and a 1M-token context window, letting readers judge the architecture's practical effect on long-context work.