Agents

AIPoolside released Laguna S 2.1, an open-weight model with 118B total parameters and 8B active per token. The author says it performs strongly on agentic coding and long-horizon tasks, and it can run on a single NVIDIA DGX Spark. Weights are on Hugging Face under the OpenMDW-1.1 license, with access also available through OpenRouter and Poolside's API.

AIJetBrains Air now connects to ACP-compatible coding agents, including GitHub Copilot CLI, OpenCode, Pi, and Cline, through the Agent Client Protocol. The release also adds Beta Java and Kotlin navigation and diagnostics powered by the IntelliJ IDEA code engine, local model support through Ollama or LM Studio, and Docker-based agent tasks on Windows.

AIAndrej Karpathy describes using /voice to ramble for about 10 minutes, sometimes as a short interview, to give an LLM context that would be tedious to type. He says LLMs reconstruct these messy streams of thought remarkably well, often returning a cleaner version than the speaker started with, which improves shared understanding and reduces later corrections.

AIJetBrains has launched JetBrains Context in early access, a repository intelligence layer that builds a semantic index so coding agents can retrieve relevant code without repeated searching. In tests on 205 SWE-bench tasks, 175 production-monorepo tasks, and 1,953 code-localization tasks, it reduced agent turns by up to 68%, latency by up to 59%, and execution cost by up to 48%. It works with Claude Code, Codex CLI, and Junie CLI at no additional cost for JetBrains AI subscribers, and it does not store source code on JetBrains Context servers.

Why it matters: The source gives benchmark figures for turns, latency, and cost, showing how repository indexing might change agent workflows on large codebases.

AICognition has acquired or welcomed the TierZero team, led by Anhang and Yun, to bring their work on automations into Devin. The goal is to help engineers spend less time handling incidents and more time building, with the source offering no further product details.

AICat Wu says she uses Claude Cowork to manage her calendar and shares her prompt. The prompt caps meetings at under 20 hours per week, excludes dinners from that cap, dedupes conflicting meetings, and learns from past declines via a refined skill, asking before updating invites.

AIMax Woolf published a short blog post examining why agent platforms have recently been issuing random weekly quota resets. The post links to the explanation at minimaxir.com for readers wanting the details.

AIDatabricks CEO Ali Ghodsi recommends an interview with Matei Zaharia about the open-source Omnigent project, which the background post describes as a meta-harness aimed at gaps in agent development. The post frames agents as still in their infancy, with development methods also immature.

AIMoonshot AI announced Kimi K3, a native multimodal model with 2.8 trillion parameters and a 1 million token context window. The announcement cites up to 6.3x faster decoding in million-token contexts and about 25% higher training efficiency, and says open weights arrive by July 27, 2026. The author, Soumith Chintala, reposted it with a brief note of congratulations.

AISable has created "Aidan," an AI employee that leads customer calls using vision, voice, video and real-time browser interaction. Frontier companies including Notion and Decagon are already using Sable to explain their products, and more than 150 companies are on its waitlist. Sequoia Capital led Sable's seed round and co-led its Series A.

AIBunkerhill Health's Carebricks platform lets health systems create and deploy AI agents across clinical and operational use cases using data hospitals already generate. Sequoia Capital backed the company at seed and is continuing to invest. At UTMB Health, Bunkerhill grew from one agent in production to more than twenty, consolidating multiple vendors' point solutions into one platform.

AIAengus has returned to Anthropic to continue a project using immersive simulated scenarios for alignment red-teaming. The work examines models from Anthropic and several other developers.

AICognition says its one-year-old merger with Windsurf has produced a more capable Devin, which now manages other Devins at a mid-to-senior engineering level, and new SWE-1.7 model, described as its most capable and efficient to date. The company reports growing from 44 to 350 people and revenue run rate from $73M to $500M+ since merging the brands.

AIArvind Narayanan's ICML keynote argues that AI's labor impact will depend on slow organizational adaptation rather than a single lab milestone. He cites reliability measurements showing agent accuracy rose much faster than reliability over the last 24 months, and points to software engineering and past technologies like electricity and ATMs. He concludes that evaluation work and human judgment will become more central as building tasks are increasingly automated.

AICognition found that Fable 5 led runs cost less than Opus 4.8 led runs on FrontierCode 1.1 when both used the same sidekick, $1.86 versus $2.04 per run. Fable 5 scored 60.7 against 54.6 for Opus 4.8 in those configurations, and it took fewer lead turns, delegated earlier, and rarely edited code itself. The post attributes the difference to delegation style rather than per-token price, and notes that the approach gives little benefit on short or serial debugging tasks.

Why it matters: The source compares lead-model delegation habits on a coding benchmark, showing how a pricier model can lower total agent cost through fewer turns and better handoffs.

AICognition's entire platform, including Devin Cloud, is now FedRAMP Class D (High) In-Process and listed on the FedRAMP Marketplace, extending FedRAMP High authorization beyond Devin Desktop (formerly Windsurf). Devin Desktop and CLI are already FedRAMP High Authorized for workloads with ITAR and DoW IL4, IL5, and IL6 requirements. The company says Devin Security Swarm can find and validate vulnerabilities and open remediation pull requests, and that fleets of Devins can upgrade legacy software 5-40x faster than humans alone.

AIClaude Code on desktop now includes an in-app browser that can open any website. Claude can read, click through, and interact with sites as it does with local dev servers, and the browser is sandboxed and configurable, letting users choose whether sessions persist.

AIJohn Schulman says Thinking Machines, founded a year and a half ago, is revisiting its original instincts after major progress in agents. The company published a written statement of what it now believes about customizable models and humans working well with AI.

AIOpenAI introduced ChatGPT Work, a new agent inside ChatGPT powered by Codex and GPT-5.6. The quoted announcement says it can take action across apps and files, stay with a project for hours if needed, and turn a goal into finished work. Fidji Simo's own post adds that the team has worked to make Chat more agentic for a while.

Why it matters: The quoted launch post names the Codex and GPT-5.6 foundation and the multi-hour task scope, which together show what changed for ChatGPT users.

AIMeta Superintelligence Labs has introduced Muse Spark 1.1, a multimodal reasoning model aimed at agentic tasks, with gains in tool use, computer use, coding, and multimodal understanding. It supports a 1 million token context window and is available in Thinking mode in the Meta AI app and on meta.ai, with developers able to access it through a public preview of the Meta Model API.

Why it matters: The post specifies Muse Spark 1.1's agent, coding, and multimodal gains and its Meta Model API preview access, which helps developers judge its fit for their workflows.

AIAnthropic will host a live walkthrough at 10am PT tomorrow showing how Claude Code progressed to multiplayer Claude Tag. The session will cover how Claude Tag works, including monitoring channels, doing proactive work, letting the whole team steer it, and remembering prior instructions.

AICursor's Aman Sanger says the new model is an enormous improvement over Composer 2.5 and was trained entirely from scratch with the SpaceXAI team. The quoted Cursor post identifies it as Grok 4.5, Cursor's most powerful model yet and the first built for more than software engineering.

AICursor co-founder Michael Truell announced Grok 4.5, a model trained with SpaceXAI that the post calls Opus-class, fast, and low cost. He says it is a significant step up over Composer 2.5 and has become the daily driver for many on the Cursor team. A benchmark table shows Grok 4.5 at 83.3% on Terminal-Bench 2.1 and 78.0% on SWE-Bench Multilingual, with the post saying more releases will follow.

AICognition launched SWE-1.7, which it says reaches frontier-level coding performance at lower cost, trained from a Kimi K2.7 base. The post describes RL methods including top-p sampling replay to preserve entropy, compressed weight deltas across multi-cluster training, and self-compaction for rollouts up to six hours. SWE-1.7 is available in Devin via Cerebras at 1000 TPS.

Why it matters: The post details entropy preservation, multi-cluster weight sync, and self-compaction, offering concrete RL training techniques for long-horizon coding agents to compare against one's own pipeline.

AIBerkeley AI Research authors argue that near-free inference will make agents the dominant workload for data systems, requiring redesign for agentic speculation, agent-run state and coordination, and agent-synthesized systems. The post cites inference prices falling 9x to 900x per year with a median near 50x, and reports that about 80-90% of sub-queries in a text-to-SQL benchmark were duplicates. It frames the three directions as data systems for, of, and by agents.

Why it matters: The piece maps three concrete data-system challenges posed by near-free inference, useful for anyone designing infrastructure for agent workloads and memory.

AILilian Weng published a new post on harness engineering for AI self-improvement. She expects harnesses to evolve toward self-improvement and enable auto-research, while smarter models keep harnesses simple. Even if many harness gains are later internalized into core models, specifying goals and context will remain necessary.

AIMeta Superintelligence Labs has released Muse Image, which can invoke search and coding tools and self-refine its generations before output. It is available today in the Meta AI app, meta.ai, Instagram Stories in the US, and WhatsApp in limited countries, with Facebook coming soon. Meta also previewed Muse Video, which is coming soon to creators and Meta AI and is reported as ranking No. 3 on Arena for text-to-video at the time of writing.

Why it matters: The source describes how search, code execution, and self-refinement change image generation, which matters to anyone comparing agentic media models with plain prompt-to-image systems.

AITufa Labs won the first $37.5K ARC-AGI-3 milestone prize with "The Duck," a small open-source LLM that plays the games by writing and running Python in a live REPL. Reki placed second with a vision-language agent using Gemma-4-31B, and md Boktiar Mahbub Murad placed third with the "forge" framework. The second and final milestone prize ends September 30.

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.

AIThe post argues that the system surrounding a base model, called the harness, increasingly determines how well AI agents deploy and improve. It reviews research where harness components such as workflows, context, and code are optimized automatically through evolutionary search and meta-agent loops. The author concludes that evaluators, memory management, and human oversight remain open bottlenecks.

AIAnthropic's Noah Zweben promoted a journey from Claude Code to Claude Tag, framing it as the future of working with AI. The related Claude post says Claude Fable 5 is now available in Claude Tag and features a conversation with Boris Cherny and Cat Wu on how the product spread from engineering across Anthropic.

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.

AIPromptArmor reports that Skills in Microsoft Copilot Cowork can call DeepSeek even when an organization has not opted into the DeepSeek Preview. The calls use the agent's own access path, so users need no API key, and a Skill built this way received a 100/100 score from Microsoft's Skill Scanner. After Microsoft removed the DeepSeek Preview setting on June 25, the report says admins had no remaining setting to block DeepSeek through the Cowork code environment, leaving disabling Cowork entirely as the only option.

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.