#Coding

AIMichael Truell of Cursor says Composer 2.5 is a significant step up from Composer 2. He adds that this is only the start of work with SpaceXAI, with more improvements expected soon. Cursor's announcement describes the model as more intelligent, better at long-running tasks, and more reliable at complex instructions, with doubled included usage for the next week.

AICognition's Devin can now spin up an Android Virtual Device, letting it build, run, and test Android applications directly on its own machine. The new emulator support gives Devin an Android equivalent of computer and browser use, so it can open apps, inspect behavior, reproduce issues, and verify changes. The feature is available now for teams using Devin.

AIAwni Hannun says Claude Code's new agent view is where he starts and manages much of his work, calling it an exceedingly useful feature. Anthropic's Claude account introduced agent view as a single list of all sessions, available now as a research preview.

AIAnthropic's Claude Security is now in public beta for Claude Enterprise customers, scanning codebases for vulnerabilities. It validates each finding to reduce false positives and suggests patches that users can review and approve. The main post from Eugene Yan simply shares the launch and expresses hope it helps people improve cybersecurity.

AIOpenAI released Auto-review in Codex, which replaces user approval at the sandbox boundary with a separate agent that approves or denies boundary-crossing actions. In internal deployment, Codex sessions stopped for human approval about 200x less often than in manual mode, and Auto-review approved around 99% of escalated actions. The post also states that Auto-review is not a guarantee of security and cannot protect against model scheming.

Why it matters: The post explains how Auto-review replaces human approval at the sandbox boundary, with internal deployment figures and stated limits that help readers judge the tradeoff for coding agents.

AIAndrej Karpathy describes a December 2025 shift in which coding agents began producing larger, more reliable chunks of work, changing programming toward orchestrating agents. He argues that models automate what can be verified and that their capability is jagged, depending on verifiability and what labs emphasize in training, so users need to stay in the loop. He also says hiring, founder opportunities, and agent-native infrastructure should adapt to this shift.

AIMercedes-Benz is deploying Cognition's Devin and Windsurf across its global engineering teams, from the United States to Europe and Asia. In a four-week pilot, Devin analyzed over 200,000 lines of COBOL code and cut modernization time from an estimated eight months to eight days. The company is now rolling out the full suite, with Windsurf for development, Devin as an autonomous cloud agent, and Devin for Terminal for the most complex tasks.

AIXiaomi released and open-sourced MiMo-V2.5-Pro, a 1.02T-parameter Mixture-of-Experts model with 42B active parameters and a 1M-token context window. The company reports gains in agentic tasks, software engineering, and long-horizon work, including a Rust SysY compiler task finished in 4.3 hours across 672 tool calls. Weights and tokenizer are on Hugging Face, and API pricing is unchanged.

Why it matters: The release pairs a 1.02T-parameter open-weight model with long-horizon agent results and token-efficiency claims, useful for judging its fit in coding and agent workflows.

AICognition has released Devin CLI, a local coding agent that runs in the shell with access to the codebase, tools, and environment. Users can choose among frontier models including Opus 4.7, GPT-5.5, and SWE-1.6, and hand sessions off to cloud agents with their own computer, which keep working after the laptop is closed.

AIMckay Wrigley says his coding split moved from 80/20 Claude/GPT to 80/20 GPT/Claude within three months, and he trusts GPT 5.5 for engineering. He still finds Claude better for non-coding agent work, calls Opus 4.7 underwhelming, and attributes Anthropic's issues to compute constraints.

AIApple has released CADD-Base-7B on Hugging Face, a 7B masked diffusion language model for code generation that uses Continuously Augmented Discrete Diffusion (CADD) to guide discrete denoising with a continuous flow-matching signal. The model loads through Transformers with trust_remote_code, and its diffusion_generate method supports CADD sampling modes "weighted" and "argmax" with alg options such as "entropy" and "maskgit_plus". The release builds on DiffuCoder and reuses Dream's modeling architecture and generation utilities.

AICognition reports that multi-agent setups work best when writes stay single-threaded and extra agents contribute intelligence instead of actions. It describes a code-review loop where a clean-context review agent catches bugs in Devin-written PRs, averaging 2 bugs per PR with roughly 58% severe. The post also says the smart-friend pattern, pairing a smaller primary model with a stronger one, has not yet worked well with asymmetrically weaker primaries and is an open training problem.

Why it matters: The post gives concrete findings on which multi-agent setups work, including clean-context code review and smart-friend escalation, and where they still fail.

AICursor's Michael Truell says the company is partnering with the SpaceX team to scale up Composer, calling it a meaningful step toward building the best place to code with AI. The quoted SpaceX post says Cursor gives SpaceX the right to acquire Cursor later this year for $60 billion, or pay $10 billion for the work together. It also cites SpaceX's Colossus training supercomputer, described as a million H100-equivalent system, as a source of training capacity.

AIEugene Yan shares a Mozilla writeup reporting that Mythos found 271 vulnerabilities fixed in Firefox 150, while Opus 4.6 found 22 fixed in Firefox 148. Mozilla quotes its finding that no category or complexity of vulnerability humans can find has been beyond the model so far.

AICognition says Windsurf 2.0 lets developers plan work locally, then send it to Devin with one click for implementation. Devin runs on its own machine, opens a pull request for review in Windsurf, and can keep working after the laptop is closed.

AIMoonshot AI released Kimi K2.6, an open-source native multimodal agentic model with 1T total and 32B activated parameters and a 256K context length. The model card reports benchmark results against GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro across agentic, coding, reasoning, and vision tasks, and supports swarms of up to 300 sub-agents.

Why it matters: The model card gives specific agent swarm scale, context length, and benchmark comparisons against several frontier models, useful for judging its coding and agent capabilities.

AICognition and Applied Compute RL-trained SWE-check, a specialized bug detection model for the Windsurf IDE. It matches frontier performance on in-distribution evals and is an order of magnitude faster with cheaper inference, though it trails frontier models on out-of-distribution evals (delta F1 0.29 versus 0.49 before training). A preview is available in Windsurf Next, with a mainstream release planned.

Why it matters: The post explains how production environment replication, reward linearization, and two-phase post-training trade bug-detection quality against latency for an IDE specialist model.

AIDevin is retiring its Core and Team plans for a new lineup of Free, Pro at $20/month, Max at $200/month, Teams with usage-based billing and an $80/month minimum, and custom-priced Enterprise. Ask Devin's Deep Mode, Devin Review after a 2-week free trial, and higher-quality DeepWiki generation will move to usage-based billing, with DeepWiki's existing generation and open-source Devin Review remaining free. Self-serve usage beyond included quota will be billed in dollars rather than ACUs.

AIAndrej Karpathy argues that people judging AI from free-tier ChatGPT or Advanced Voice Mode miss the strong capabilities of current agentic models like OpenAI Codex and Claude Code. He says gains are "peaky," concentrated in verifiable technical domains like programming and math that suit reinforcement learning and attract B2B investment, while writing and everyday advice improve less. Those who use frontier agentic tools professionally in these fields see far greater capability, which is why the two groups talk past each other.

AIMiniMax has released MiniMax-M2.7 on Hugging Face, describing it as its first model to participate in its own evolution. The source reports 56.22% on SWE-Pro, 46.3% on Toolathon, and 62.7% on MM ClawBench, and says an internal version autonomously optimized a programming scaffold over 100+ rounds for a 30% performance improvement.

Why it matters: The source ties its benchmark claims to a self-evolution process and a named comparison set, which helps readers weigh how the reported gains were achieved.

AICognition describes how Devin handles COBOL modernization at several Fortune 500 companies, citing a shortage of COBOL developers and 68% failure rates for such efforts. The post identifies three obstacles for agents: untraceable data across copybooks, little COBOL in model training, and no way to run code on Linux-based VMs. It says Devin succeeds on documentation, batch migrations, and large-scale refactoring, while transactional workloads remain out of reach.

Why it matters: The post explains why agents struggle with COBOL and which workloads they can migrate, giving a framework for judging where automation fits legacy systems.

AIZ.ai's GLM-5.3 delivers a 50% coding gain over GLM-5.2 on Z.ai Code Bench, reaching open-source state-of-the-art on public benchmarks including Terminal Bench 3.0. GLM-5.3-Flash uses 320B total parameters with 18B activated, combining linear and sparse attention to reduce compute and KV-cache needs. GLM-5.2 supports a 1M lossless context window for long-horizon tasks.

AIWindsurf has made SWE-1.6, its model for software engineering agents, generally available, with the company saying it improves on the SWE-1.6 Preview by reducing overthinking, looping, and sequential tool calls. The model is free for three months, with a free version offered at 200 tok/s through Fireworks and a faster paid version at 950 tok/s through Cerebras.

AIZ.ai has released GLM-5.1, its next-generation flagship model for agentic engineering, with stronger coding than GLM-5. The model is described as staying effective over longer agentic tasks, sustaining optimization over hundreds of rounds and thousands of tool calls. The release lists benchmark results including SWE-Bench Pro at 58.4 and Terminal-Bench 2.0 at 63.5, and local deployment is supported through SGLang, vLLM, xLLM, Transformers, and KTransformers.

Why it matters: The release gives benchmark tables against several rival models, letting readers compare GLM-5.1's coding and agentic results with GLM-5 and frontier systems.

AIAI Futures Project moved Daniel Kokotajlo's Automated Coder median from late 2029 to mid 2028 and Eli's from early 2032 to mid 2030. The main reasons cited are a faster METR time horizon doubling time and the impressive results of Claude Opus 4.6. The authors also say progress in agentic coding has been faster than expected over the past 3 to 5 months.

AIMistral AI released Mistral Medium 3.5, a dense 128B model with a 256k context window that handles instruction-following, reasoning, and coding in a single set of weights. It replaces Mistral Medium 3.1, Magistral, and Devstral 2, and reasoning effort is configurable per request. The model accepts text and image input and is released under a Modified MIT License that excludes companies with large revenue.

Why it matters: The release merges instruction, reasoning, and coding into one 128B model with per-request reasoning control, giving developers one set of weights to compare against separate specialized models.

AIAnthropic's Labs team describes a three-agent harness with planner, generator, and evaluator agents for building full-stack applications over multi-hour autonomous coding sessions. The evaluator uses Playwright to test the running app against sprint contracts, and a retro game maker built with the harness worked end to end where a single-agent run's core feature did not. The author later removed the sprint construct and kept only the components still needed on Opus 4.6.

Why it matters: The post shows how a generator-evaluator loop, with explicit grading criteria and a tuned QA agent, turned a solo run's broken output into a working app, and how the harness was pruned as models improved.

AICursor is increasing capacity for Composer 2, offering 2x more usage all weekend. Users are invited to try it out.

AIAman Sanger says Cursor's team evaluated many base models on perplexity-based evals and found Kimi k2.5 the strongest. Composer 2 was then built with continued pretraining and a 4x scale-up of high-compute RL, with Fireworks providing inference and RL samplers. The author admits Cursor should have named the Kimi base in its launch blog and says it will do so for the next model.

AICursor has released Composer 2, its domain-specific model for coding agents. The company says its strategy combines top API models with its own specialized models to build the most useful coding agents.

AICursor says its Composer 2 anniversary marks one year of large model training, with a team of about 40 researchers and engineers now dedicated entirely to software engineering. The company states that every FLOP, token, parameter, and researcher is focused on coding rather than general assistant tasks. Composer 2 is now available in Cursor.

AIDevin can now break large tasks into scoped pieces and delegate them to a team of managed Devins that run in parallel. Each managed Devin runs in its own isolated virtual machine with its own terminal, browser, and development environment, and has its own session link. The main coordinator session monitors progress, resolves conflicts, and compiles results, and managed Devins are available now for all users.

Why it matters: The post explains how a coordinator session splits work across isolated managed sessions, giving readers a concrete pattern for running agent tasks in parallel.

AIMckay Wrigley says gpt-5.4 at xhigh reasoning effort has fundamentally changed how ambitious he is, and he now treats that shift as his favorite benchmark. The post gives no benchmark scores, prices, or other concrete figures.

AIMiniMax says M2.7 is its first model to deeply participate in its own evolution, building agent harnesses and running reinforcement learning experiment workflows. The post reports 56.22% on SWE-Pro, 55.6% on VIBE-Pro, 57.0% on Terminal Bench 2, and a 30% improvement on an internal evaluation set after more than 100 autonomous optimization rounds. It also states that M2.7 handles 30%-50% of its research team's workflow, though human researchers still make critical decisions.

Why it matters: The post ties M2.7's self-evolution claims to specific benchmark numbers and workflow details, helping readers judge how much of the iteration loop is autonomous.

AIXiaomi announced MiMo-V2-Pro, a flagship foundation model for agent workloads with over 1T total parameters, 42B active, and up to 1M-token context. It ranks 8th worldwide and 2nd among Chinese LLMs on the Artificial Analysis Intelligence Index, and its API is publicly available with usage-tiered pricing.

Why it matters: The post gives benchmark placements, parameter scale, context length, and tiered API pricing, so readers can compare it against Claude and GPT models on concrete terms.

AIApple has released apple/SimpleSD-30B-instruct, a research checkpoint built on Qwen that uses Simple Self-Distillation to improve code generation without rewards, verifiers, or teacher models. On LiveCodeBench, the model scores 55.3% pass@1 on LCBv6 versus 42.4% for its base, Qwen3-30B-A3B-Instruct-2507. The checkpoints are for reproducibility, not optimized Qwen releases, and are available under the Apple Machine Learning Research Model License.

AIApple has published SimpleSD-4B-thinking on Hugging Face, a research checkpoint built on Qwen that improves code generation through Simple Self-Distillation without rewards, verifiers, teacher models, or reinforcement learning. On LiveCodeBench, it lifts Qwen3-4B-Thinking-2507 from 54.5% to 57.8% pass@1 on LCBv6 and from 59.6% to 63.1% pass@1 on LCBv5. The model is released as a reproducibility checkpoint under the Apple Machine Learning Research Model License, not as an optimized Qwen release.

AIApple has released SimpleSD-4B-instruct on Hugging Face, a research checkpoint fine-tuned from Qwen3-4B-Instruct-2507 on its own sampled outputs to improve code generation. On LiveCodeBench, the model scores 41.5% pass@1 on LCBv6, up from the base model's 34.0%, and 45.7% pass@1 on LCBv5, up from 34.3%. The model is released under the Apple Machine Learning Research Model License and is intended for reproducibility rather than as an optimized Qwen release.