Coding

AIAman Sanger of Cursor says the company trained what it calls the best coding model in the world under 1T parameters. He invites people to try it, and Cursor's background post names it Composer 1.5, which it says balances intelligence and speed.

AIMax Woolf reports that after GPT-5.2-Codex crashed and failed in his testing, GPT-5.3-Codex performed like a leap from Sonnet 4.5 to Opus 4.5 in coding. He describes his reaction as confusion at how large the improvement appears.

AIAnthropic reports that resource configuration alone can move Terminal-Bench 2.0 scores by up to 6 percentage points, with infra error rates falling from 5.8% under strict enforcement to 0.5% when uncapped. Above about 3x the per-task specs, extra headroom starts letting agents solve tasks they previously could not, so limits can change what the eval measures.

Why it matters: The source shows how container resource limits shift agentic coding scores, which helps readers interpret small leaderboard gaps and set up evals more consistently.

AINicholas Carlini of Anthropic's Safeguards team describes an agent-team setup where 16 Claude instances worked in parallel on a shared codebase without human intervention to write a Rust-based C compiler. Over nearly 2,000 Claude Code sessions costing about $20,000 in API fees, the team produced a 100,000-line compiler that can build Linux 6.9 on x86, ARM, and RISC-V. The post focuses on harness design, including high-quality tests, lock files for task claiming, GCC as a reference oracle for the kernel, and the limits the project reached.

Why it matters: The post shows concrete harness design choices for long-running agent teams, including test design, locking, and parallel work division, that readers can adapt to their own autonomous projects.

AIThe Codex app, a desktop app for agentic coding, launched with features including worktrees for parallel agent sessions, Skills, MCP connections, compaction, and automations. The author, who says they stopped opening their AI IDE for four days, now manages multiple Codex agents as a reviewer rather than writing code directly.

AICognition is opening a London office to expand rollout of Devin, its autonomous software engineering agent, to leading European businesses. The company says finance has emerged as a clear use case, with Goldman Sachs, Santander, Citi, and BNY among partners using Devin for modernization, migration, security remediation, and codebase documentation.

AICognizant is deploying Cognition's Devin autonomous software engineer and Windsurf agentic IDE across its engineering organization and global client base. Engineers already use Windsurf for agent-assisted coding and are exploring Devin for end-to-end tasks such as code migration, refactoring, testing, and maintenance. Cognition will embed forward-deployed AI engineers to support project selection, engineer enablement, and ROI measurement.

AIAi2's Open Coding Agents family, with SERA as its first release, was built by Tim Dettmers and collaborators on 32 GPUs. The method generates synthetic bug trajectories with soft verification, comparing patches by line overlap instead of running tests. The post reports that a 32B model fine-tuned on about 7,000 trajectories for one private repository matched its GLM 4.5-Air teacher, and that the baseline costs $500 to run.

AICursor introduced Cursor Blame, a feature that records the "why" behind code changes so teammates can trace code lineage. The company says this context can also help future agents make better decisions, as part of its agent-first rethinking of software engineering primitives.

AICognition introduced Devin Review, a free early-release code review tool that works on any public or private GitHub PR, with features for organizing diffs, chatting about changes, and flagging AI-detected bugs. The company says code review, not code generation, is now the bottleneck as coding agents increase the volume and size of pull requests.

AIFactory's new Agent Readiness tool evaluates repositories across eight technical pillars and five maturity levels, using 60+ binary criteria run via the /readiness-report command. The company says it can also open pull requests to fix foundational gaps such as missing AGENTS.md files, linter configuration, and pre-commit hooks. Factory says scores are now more consistent, with variance dropping from an average of 7% to 0.6%.

AIAman Sanger of Cursor argues that synchronous coding is nearing diminishing returns to intelligence, with over 95% of queries expected to gain little from smarter models within months. He contends that extra intelligence matters mainly for asynchronous tasks that take developers hours, while UI work is bottlenecked by user intent rather than model capability. He is therefore excited about frontier models running at Composer-1 speed.

AIHamel Husain says he stopped using nbdev, a literate programming environment he helped build and maintain, because AI coding tools struggle with its notebook-to-library workflow. He now uses Amp, Cursor, and Claude Code, and reserves notebooks for data analysis, machine learning, and exploratory work. He also favors conventional stacks such as Next.js for web development, arguing that AI performs best on widely used languages with abundant training data.

AITim Dettmers, a professor who has used Claude Code for eight months to automate his own work, argues that more than 90% of code and text should be written by agents. He says the coding-focused hype on Twitter overstates parallel sessions and autonomy, which translate poorly to most non-software tasks. The post offers a balanced guide to what actually works in agent-based automation.

AIMiniMax has released MiniMax-M2.1 model weights on Hugging Face, with API access on the MiniMax Open Platform and the MiniMax Agent product. The company reports gains over M2 on coding and agent benchmarks such as SWE-bench Verified (74.0) and VIBE average (88.6), and says it outperforms Claude Sonnet 4.5 on multilingual scenarios.

Why it matters: The release pairs open weights with a broad benchmark table against Claude and GPT models, letting readers compare coding and agent claims directly.

AIAman Sanger, owner of Cursor, argues that as agents help write an order of magnitude more code, review becomes the main bottleneck in software engineering. The post's context notes that Graphite is joining Cursor, which is relevant to code review workflows.

AIAndrej Karpathy's year-in-review lists the LLM paradigm changes he found most notable in 2025. He highlights Reinforcement Learning from Verifiable Rewards (RLVR), which drove most capability gains as labs ran longer RL training, and describes LLM intelligence as jagged, strong in verifiable domains and weak elsewhere. He also covers Cursor-style LLM apps, Claude Code running on the user's computer, vibe coding, and the case for a visual LLM GUI.

AIXiaomi released and open-sourced MiMo-V2-Flash, a Mixture-of-Experts model with 309B total and 15B active parameters, under the MIT license. The company reports 73.4% on SWE-Bench Verified, the top score among open-source models, and inference at 150 tokens per second for $0.1 per million input tokens and $0.3 per million output tokens. It supports a hybrid thinking mode and a 256k context window.

Why it matters: The post gives architecture, speculative decoding speedup, and pricing figures, which help readers judge how the efficiency claims are achieved and what they cost.

AIAndrej Karpathy built hn-time-capsule, a tool that feeds each December 2015 Hacker News front-page article and its comment thread to GPT-5.1 Thinking for a retrospective analysis. The project, written with Claude Opus 4.5 in about three hours, processes 930 articles at a cost of about $58 and roughly one hour. Results include prescience and wrongness grades for commenters, and the project is hosted on his website with the intermediate data available for download.

AIGoogle's Gemini 3 Deep Think mode is now available in the Gemini app for Ultra users. The post says it uses parallel thinking for difficult coding and scientific tasks and builds on technology that reached gold-medal level at the ICPC World Finals and IMO.

AIThe main post argues that AI has moved the bottleneck from execution to human judgment, vision, taste, and context. AI can explore options but cannot determine which is right, so specialization now lies in judgment rather than execution. The background post, by designer @ryolu_, adds that small teams with overlapping skills may outperform larger specialist teams coordinating handoffs.

AICognition's 2025 performance review says Devin works best on clear, verifiable tasks such as migrations, vulnerability fixes, and unit tests. The company reports a 67% PR merge rate, up from 34% last year, and cites a bank that cut migration time per file from 30-40 hours to 3-4 hours. It also says Devin struggles with ambiguous requirements, mid-task scope changes, and soft-skill work that still needs human engineers.

Why it matters: The report pairs concrete migration, vulnerability, and test-coverage figures with named weaknesses, letting engineering leaders judge where an agent fits in their own workflow.

AIAman Sanger of Cursor says the team is hard at work on Composer 2. The post gives no features, benchmarks, or release timing, though it follows Cursor's $2.3B Series D raise and over $1B in annualized revenue.

AIWindsurf has launched Codemaps, AI-annotated structured maps of a codebase powered by SWE-1.5 and Claude Sonnet 4.5, which users can generate from a task prompt using a Fast (SWE-1.5) or Smart (Sonnet 4.5) model. Codemaps links grouped code sections to exact lines and can be referenced in Cascade with @{codemap} to give agents more specific context.

AIChip Huyen argues that many AI product failures stem from user experience, data quality, and organizational structure rather than the AI itself. She cites a chatbot whose traction improved after adding pre-populated questions and a voice option for users whose hands were busy, and a lead scoring model that was broken because marketing wasn't asking the right questions. She also notes that senior engineers gain the most from AI coding while resisting it more, and recommends building small tools for daily frustrations to solve the "idea crisis."

AICognition has released SWE-1.5, a model optimized for software engineering that it says reaches near-frontier coding performance while running at up to 950 tok/s with Cerebras inference. The company reports it is 6x faster than Haiku 4.5 and 13x faster than Sonnet 4.5, and it is available now in Windsurf. The post's SWE-Bench Pro chart places SWE-1.5 at 40.08%, behind Sonnet 4.5 at 43.60%, and it notes that the model was trained with reinforcement learning on the Cascade agent harness.

Why it matters: The post pairs a benchmark chart with a 950 tok/s speed claim and describes how harness, RL environments, and inference were co-designed, useful context for judging the speed-versus-quality tradeoff.

AICognition says its autonomous coding agent Devin can complete a .NET Framework to .NET Core migration in as little as two weeks, using a Strangler Fig approach adapted from Jimmy Bogard's guide. The post says Devin handles planning via Ask Devin and DeepWiki, dependency sharing, controller and view conversion, and session state adaptation through a remote app.

AIA hiring manager told Chip Huyen that a software engineering candidate who has not experimented with vibe coding is a red flag. Huyen posted the remark on X and asked for readers' thoughts.

AICognition introduces SWE-grep and SWE-grep-mini, fast agentic models trained with reinforcement learning for multi-turn context retrieval in coding tasks. The company says they match frontier coding models at retrieval while taking an order of magnitude less time, and they power the Fast Context subagent in Windsurf. The models issue up to 8 parallel tool calls per turn within 4 turns, and Cerebras serves SWE-grep-mini at over 2,800 tokens per second and SWE-grep at over 650 tokens per second.

Why it matters: The post explains the speed-intelligence tradeoff in agentic code search, showing how parallel tool calls and RL training change the cost of retrieving context for coding agents.

AICognition rebuilt its Devin coding agent for Claude Sonnet 4.5, reporting 2x faster performance and 12% better results on its Junior Developer Evals, now available in Agent Preview. The team found the model is aware of its context window, which led to premature wrap-up behavior that they countered with repeated prompts and a 200k usage cap within a 1M token beta.

Why it matters: The post explains which agent behaviors changed under Sonnet 4.5, such as context-window awareness and note-taking, that forced a rebuild rather than a simple model swap.

AICognition says Claude Sonnet 4.5 is available in Devin starting today, improving its planning performance by 18% and end-to-end eval scores by 12%. Cognition says the model's testing of its own code lets Devin run longer and handle harder tasks.

AICognition, maker of the AI software engineer Devin, raised over $400M at a $10.2B post-money valuation led by Founders Fund. The company says its acquisition of Windsurf more than doubled its ARR, with combined enterprise ARR up over 30% in the seven weeks after the deal. It also reports Devin ARR grew from $1M in September 2024 to $73M in June 2025, with total net burn under $20M.

AICognition Blog published "Coding Agents 101: The Art of Actually Getting Things Done" on June 26, 2025, as a guide to using coding agents effectively. The available source text contains only navigation and a list of other Cognition posts, so no specific techniques, features, or results can be verified from it.

AICognition is bringing back its Devin Open Source Initiative, offering $500 in Devin ACU credits to open-source GitHub projects with over 100 forks. Projects below that threshold will still be considered. Eligible projects must have an OSI-approved license and be actively maintained, and maintainers can apply through a linked form.

AICognition has released Devin 2.1, which reports its confidence in completing tasks using green, yellow, and red ratings. The company says green scores led to twice the likelihood of a merged PR compared with red, and Devin now also answers codebase questions and scores Linear and Jira issues.

Why it matters: The post explains how Devin now shows confidence scores and asks clarifying questions, which changes how teams can decide which tasks to hand over.

AIStanford and Cognition AI researchers introduced Kevin-32B, a 32B-parameter model trained with multi-turn reinforcement learning to write CUDA kernels. On KernelBench, it solves 89% of tasks at best@16 and achieves 65% average correctness over eight refinement steps, versus 53% for o4-mini and 51% for o3. Its best@16 speedup is 1.41x, and multi-turn training outperforms single-turn training as refinement steps increase.

AICognition has launched DeepWiki, a free public version of its Devin Wiki and Devin Search tools that helps developers understand codebases. Users can view docs for any repo by replacing github.com with deepwiki.com in the URL, and more than 50,000 top public GitHub repositories are already indexed. Private repositories require a Devin account.

AICognition has released Devin 2.0, a new agent-native IDE experience with a flexible plan starting at $20. The update lets users run multiple parallel Devins, each with its own cloud-based IDE, and adds Interactive Planning, Devin Search, and Devin Wiki.

Why it matters: The release adds planning, codebase search, and auto-generated wikis to Devin, showing how an agent can prepare work before executing it.