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Coding assistants, vibe coding, code model evaluations, and changes to software development workflows.

114 picksPast 30 days: 35 itemsTotal: 719 items

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Top picks archive · Page 5

May 31

May 31SunItems 81–100
  1. MiniMax BlogAI score82

    MiniMax M3 releases with 1M context, native multimodality and sparse attention

    MiniMax 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.

    AIWhy 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.

May 28

May 28Thu
  1. Cognition Blog (Devin, Windsurf)AI score62

    Devin Tests Its Own Code Changes in the Cloud and Returns Proof

    Cognition describes autonomous testing in Devin, where the agent writes a source-grounded test plan, operates the app through computer use, and returns labeled screenshots and an annotated video. Login steps are handled by a deterministic testing skill, and the company says test runs approved per day more than doubled in recent months. Known limits include timing errors with transient UI elements and models sometimes triggering states through JavaScript instead of clicking the interface.

    AIWhy it matters: The post explains how computer use, test plans, deterministic login scripts, and annotated recordings let Devin verify its own code changes end to end.

May 19

May 19Tue
  1. koray kavukcuogluAI score72

    Google's Gemini 3.5 Flash beats Gemini 3.1 Pro on coding and agentic benchmarks

    Google's Gemini 3.5 Flash outperforms Gemini 3.1 Pro on Terminal-Bench 2.1 (76.2%), GDPval-AA (1656 Elo), and MCP Atlas (83.6%). The post also claims it is 4x faster than other frontier models, or 12x in Antigravity, and reports 83.6% on MMMU-Pro for multimodal performance.

    AIWhy it matters: The post gives specific benchmark scores against Gemini 3.1 Pro, letting readers compare coding, agentic, and multimodal results directly.

Apr 30

Apr 30Thu
  1. OpenAI Alignment Research BlogAI score79

    OpenAI's Auto-review lets Codex agents act without constant human approval

    OpenAI 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.

    AIWhy 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.

Apr 26

Apr 26Sun
  1. Xiaomi MiMoAI score87

    Xiaomi releases open-source MiMo-V2.5-Pro for long-horizon agentic coding

    Xiaomi 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.

    AIWhy 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.

Apr 21

Apr 21Tue
  1. Cognition Blog (Devin, Windsurf)AI score72

    Cognition says multi-agent systems work when only one agent writes

    Cognition 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.

    AIWhy 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.

Apr 14

Apr 14Tue
  1. Moonshot AI (Kimi) · new models on Hugging FaceAI score78

    Moonshot AI releases open-source Kimi K2.6 multimodal agentic model

    Moonshot 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.

    AIWhy 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.

Apr 13

Apr 13Mon
  1. Cognition Blog (Devin, Windsurf)AI score62

    Cognition introduces SWE-check, a fast RL-trained bug detection model for Windsurf

    Cognition 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.

    AIWhy 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.

Apr 8

Apr 8Wed
  1. MiniMax · new models on Hugging FaceAI score78

    MiniMax releases open-weight MiniMax-M2.7 with agent and coding gains

    MiniMax 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.

    AIWhy 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.

Apr 7

Apr 7Tue
  1. Cognition Blog (Devin, Windsurf)AI score70

    How Devin Is Modernizing COBOL at Fortune 500 Companies

    Cognition 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.

    AIWhy 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.

Apr 3

Apr 3Fri
  1. Z.ai (GLM) · new models on Hugging FaceAI score73

    Z.ai releases GLM-5.1, a flagship model for agentic engineering

    Z.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.

    AIWhy 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.

Mar 31

Mar 31Tue
  1. Mistral AI · new models on Hugging FaceAI score76

    Mistral Medium 3.5 releases as a 128B dense merged model with vision

    Mistral 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.

    AIWhy 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.

Mar 23

Mar 23Mon
  1. Anthropic EngineeringAI score78

    Anthropic shows a three-agent harness for long-running app development

    Anthropic'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.

    AIWhy 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.

Mar 18

Mar 18Wed
  1. Cognition Blog (Devin, Windsurf)AI score72

    Devin can now break tasks down and run a team of managed Devins

    Devin 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.

    AIWhy 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.

Mar 17

Mar 17Tue
  1. MiniMax BlogAI score63

    MiniMax M2.7 takes part in its own model and harness evolution

    MiniMax 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.

    AIWhy 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.

  2. Xiaomi MiMoAI score80

    Xiaomi MiMo-V2-Pro Flagship Model Targets Agent Workloads With 1M Context

    Xiaomi 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.

    AIWhy 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.

Feb 26

Feb 26Thu
  1. Cognition Blog (Devin, Windsurf)AI score67

    How Cognition Uses Devin to Build Devin Across Slack, Linear, and Code Review

    Cognition reports merging 659 Devin PRs into its own codebase last week, up from 154 in its best week in 2025. The post describes internal workflows across web, Slack, Linear, CLI, and API, including Devin Review for PR diffs and bug catching, a daily design system audit, automated bug triage on Linear, and DANA for data analysis.

    AIWhy it matters: The post shows concrete workflows for using Devin across Slack, Linear, and code review, with specific usage figures that help teams judge fit for their own engineering processes.

Feb 12

Feb 12Thu
  1. MiniMax · new models on Hugging FaceAI score88

    MiniMax releases M2.5 model with 80.2% on SWE-Bench Verified

    MiniMax has released M2.5, which it says reaches 80.2% on SWE-Bench Verified and 76.3% on BrowseComp with context management. The company reports 37% faster end-to-end runtime than M2.1 on SWE-Bench Verified and prices M2.5 at $1 per hour at 100 tokens per second, with a 50 tokens per second version at $0.30 per hour. Weights are available on Hugging Face, with inference support listed for SGLang, vLLM, Transformers, and KTransformers.

    AIWhy it matters: The source gives benchmark scores against Claude and GPT models plus per-task token and runtime figures, so readers can weigh the cost-speed tradeoff directly.

Feb 10

Feb 10Tue
  1. Z.ai (GLM) · new models on Hugging FaceAI score72

    Z.ai releases GLM-5, a 744B-parameter open model for agentic engineering

    Z.ai launches GLM-5, scaling from 355B to 744B total parameters with 40B active and pre-training data from 23T to 28.5T tokens. The model integrates DeepSeek Sparse Attention to reduce deployment cost and reports strong results on reasoning, coding, and agentic benchmarks against GLM-4.7, DeepSeek-V3.2, Kimi K2.5, and several frontier models.

    AIWhy it matters: The source gives concrete scale, data, and benchmark comparisons against named frontier models, showing where GLM-5 sits among open-source and proprietary systems.

Feb 4

Feb 4Wed
  1. Anthropic EngineeringAI score72

    Anthropic finds container resource limits can shift agentic coding eval scores

    Anthropic 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.

    AIWhy 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.