Inkling 2 Small, GPT-6 Cyber, Managed Deep Agents

Thinking Machines’ Inkling 2 Small — a 594B/17B-active MoE trained in 68 hours, shipping Apache 2.0 weights next week — is the week’s landmark alongside Ricursive’s Ripple-65B trained for $32M beating Nemotron 3 Ultra. Open-weight coding frontier is now a credible bet for your LiteLLM gateway. Meanwhile OpenAI shipped GPT-6 Cyber and used it to autonomously patch 948 Linux kernel privilege-escalation bugs — the vulnerability-apocalypse narrative Nathan Lambert flagged just got teeth. Claude Code is now at v2.1.318 with per-subagent effort controls and pooled org budgets. If you run an overnight agent factory on Claude Code + Cursor, this week forces two decisions: which open-weight coder enters your rotation, and how you harden against AI-assisted exploitation of your own dependencies.

Launches & releases this week

Models

Inkling 2 Small — Thinking Machines’ 594B-parameter MoE with 17B active, trained in 68 hours on 12T tokens; Apache 2.0 weights land next week. (TLDR AI)
GPT-6 Cyber — OpenAI’s cybersecurity model finds exploitable vulnerabilities and generates patches while staying within researcher-defined scope. (OpenAI blog)
Ripple-65B Preview — Open-weight 65B model trained for $32M tops TerminalBench 2.1, beating Nemotron 3 Ultra 550B and Inkling 975B on agentic coding. (TLDR AI)
Kimi K3.1 Pro — Moonshot’s 3T-class multimodal model with 1M-token context, now on Vercel AI Gateway via multiple ZDR providers. (TLDR AI)
Gemini Omni 1.2 Flash — Google’s video-gen Flash variant handles 3-15s clips with native audio and up to 30s extensions; live on Vercel AI Gateway. (Google DeepMind blog)
Mistral Large 4 OSS — Trillion-parameter open-weight model aimed at self-hosted agentic coding and cybersecurity, comparable to GLM-5.3. (TLDR AI)
SWE-2 Flash — Cognition’s coding model introduces a new mid-training stage before RL. (TLDR AI)
Tencent Hy4 — Open-source multimodal LLM with CLI and GUI interaction; weights and system cards releasing shortly. (TLDR AI)

Features & Tools

Claude Message Threads — Public beta lets agents send incremental messages instead of resending history; cuts median TTFT by ~35% on 500K–1M token requests. (TLDR AI)
Cursor Live Apps — Describe a tool in Cursor; it builds, provisions a database, runs security checks, and deploys to a shareable link. (Cursor changelog)
Vercel Security Review — Public beta scans every push to connected GitHub repos for vulnerabilities, surfacing findings as PR comments and dashboard entries. (Vercel blog)
Vercel MCP CIMD — Any MCP client can now connect to Vercel MCP via Client ID Metadata Documents, removing the manual allowlist review. (Vercel blog)
Images 2.6 — 50% faster image generation plus GIF output and frame-level editing, live globally in ChatGPT and Codex. (TLDR AI)

Products

Managed Deep Agents — LangChain’s hosted runtime for deep agents with durable execution, sandboxes, browser use (Stagehand) and async subagents; private beta. (LangChain blog)

Other Releases

Claude Code v2.1.318 — Adds per-subagent effort via $.model.fork, CLAUDE_CODE_SUBAGENT_EFFORT, pooled org budgets, and AWS assume_role for per-dev cost attribution. (GitHub: anthropics/claude-code)

Stories to follow

Open-weight coders close the gap

Three frontier-class open-weight drops in one week — Inkling 2 Small, Ripple-65B, Mistral Large 4 — all explicitly targeting agentic coding, with Kimi K3.1 Pro and Hy4 adding multimodal ammunition. Ripple’s $32M training cost and TerminalBench lead against models 8× its size is the number to remember. For a team already routing via LiteLLM, the question shifts from ‘can we use open weights for coding?’ to ‘which one earns a slot in the overnight agent factory alongside Claude?’

Inkling 2 Small — 594B MoE / 17B active, Apache 2.0 weights releasing after safety testing. (TLDR AI)
Ripple-65B Preview — $32M training run tops TerminalBench 2.1, beats 550B+ open-weight rivals on coding. (TLDR AI)
Mistral Large 4 OSS — Trillion-parameter open-weight release for self-hosted agentic coding and cybersecurity. (TLDR AI)
Kimi K3.1 Pro — 3T-class multimodal model with 1M-token context, now on Vercel AI Gateway. (TLDR AI)

AI-generated code is now a security primitive

GPT-6 Cyber autonomously patched 948 Linux kernel privilege-escalation bugs in a single run. Epoch showed open-weight GLM 5.3 can turn published security fixes into working exploits before users patch. Anthropic published poisoning research showing 0.1% of pretraining data can plant RCE backdoors in coding agents, and red-teamers found 11 ways past Codex’s Auto-review. DoW Instruction 8430.01 now treats AI-generated code as unverified until proven otherwise. The verification burden on 22,000-line PRs just became an auditable compliance requirement, not a management theory.

A thousand fewer paths to root — OpenAI’s autonomous pipeline validated and patched 948 Linux LPE vulnerabilities in one run. (OpenAI blog)
AI turns security fixes into exploits before patches ship — GLM 5.3 weaponises published Firefox security fixes before users receive updates. (TLDR AI)
Poisoning 0.1% of pretraining data planted RCE backdoor — Trigger paths in web data made a 4B coding agent run attacker commands 92% of the time. (TLDR AI)
11 ways past Codex Auto-review — Red team found sandbox and monitor bypasses letting misaligned agents run arbitrary code. (TLDR AI)
DoW Instruction 8430.01 on AI-generated code — Pentagon requires every line of AI-written code to be reviewed and tested before acceptance. (GitLab blog)

Agent harnesses become the product

LangChain shipped Managed Deep Agents with durable execution, Stagehand-based browsers and async subagents. Framer’s Harness 2 streams and executes JavaScript as it arrives, cutting cost 33%. Factory’s Droid 1.0 argues a model-agnostic harness beats model-tuned ones. Microsoft’s Copilot Cowork migrated Python→Rust live. The ‘overnight agent factory’ pattern is now a product category — and the harness, not the model, is where the leverage sits.

Managed Deep Agents — Hosted runtime with sandboxes, browser use and async subagents; private beta. (LangChain blog)
Harness 2 streams and executes — Executes JS as it streams; up to 33% cost reduction and 20% faster workflows. (TLDR AI)
Droid 1.0: Building a Frontier Harness — Argues model-agnostic harness design generalises better than tuning to one model. (TLDR AI)
Copilot Cowork Python→Rust — Microsoft swapped runtime live in 4.5 months via narrow agents and clear specs. (TLDR AI)
Agent State Machines — Enforcing permissions, approvals and completion criteria outside the prompt. (TLDR AI)

AI-native operating models get real numbers

Vercel’s COO: 10 SDRs down to 1, 93% of support cases handled by AI, SDR quotas up 30%. SaaStr itself replaced a 4-person marketing team with one superagent. Zapier builds partner demos in under two hours with v0. Pragmatic Engineer reports Anthropic killed the LeetCode interview. This is what the Act-2-style restructure looks like at operational altitude — before/after numbers from named companies, not manifestos. Worth tracking against GitLab’s blueprint.

Vercel’s COO on AI-driven org reshape — 10 SDRs to 1, 93% of support automated, SDR quotas up 30%, any tool without an API gets cut. (SaaStr (Jason Lemkin))
Zapier internal apps in hours — Partner demos in under 2 hours with v0; 3.3M sandboxes in a month. (Vercel blog)
SaaStr’s 4-person marketing team → one superagent — WSJ-profiled case of a specialised agent replacing a marketing headcount pool. (SaaStr (Jason Lemkin))
Anthropic kills the coding interview — LeetCode-style loop dropped as AI makes it uninformative; new signal required. (The Pragmatic Engineer (Gergely Orosz))

What I’m watching

Recursive self-improvement signalling — Anthropic’s IPO filing explicitly flags approaching RSI; METR’s independent assessment shows AI R&D at Anthropic accelerating at ~2× the prior rate. Watch, don’t act — but track the vocabulary.
- Anthropic warns it’s close to RSI (TLDR AI)
- METR assessment of Anthropic R&D (TLDR AI)
Weight-space programming — The claim that frontier models can read and edit model weights is overstated, but the underlying technique (targeted weight edits unlocking latent skills) is real and worth watching for interpretability tooling.
- Qwen 3.8 27B interprets model weights locally (Simon Willison)
- The Astra weight-editing claims are misleading (TLDR AI)
- Unlocking hidden skills via weight tuning (TLDR AI)
Agent identity and delegation on the web — Cloudflare’s trust framework, OpenAI’s shared robots.txt signal, and Vercel MCP’s CIMD support are the early scaffolding for how agents authenticate to third-party systems — relevant for anyone exposing or consuming MCP servers.
- Cloudflare’s Trust Framework for AI Agents (TLDR AI)
- Shared robots.txt AI training control (TLDR AI)
- Vercel MCP supports CIMD (Vercel blog)

deepseek-ai/dsh-superagent-prototype

20.1k★ · TypeScript Towards a More Autonomous Agent System

storytold/craft-anything

17.4k★ · Rust Craft Anything: understand how any software works, and craft your own.

patch-gg/pixelfx

5.5k★ · JavaScript · agent-skills animation canvas claude-code-plugin gamedev 244 pixel art effects, every one a small piece of code. Plus a Claude Code skill to make your own.

KKKKhazix/agent-autoloop

3.6k★ · TypeScript · ai-agents automation claude-code cli codex 让你的命令行 Agent 在后台自己接活、自己跟进到底；平时不烦你，遇到拿不准的事才发卡片找你拍板。

zortos293/Lichii

3.5k★ · C++ VERY experimental Nintendo Switch 2 emulator

Read this weekend

How software engineering is changing: a tale of two eras

Gergely Orosz’s reader-essay winner on how building software has actually changed in the agentic era — the kind of practitioner-grounded piece worth sitting with on Saturday if you’re writing publicly on the same shift.

Quote of the week

More broadly, the use of LLMs erodes trust in documentation, in software, and in the organizations which produce them. Perhaps engineers will begin to treat any new software project with suspicion — the signals we used to rely on as markers of a mature system are now easier to counterfeit.

— Kyle Kingsbury (Jepsen), via Simon Willison · link

Sources unavailable this week: Every — Chain of Thought (Dan Shipper), r/ChatGPTCoding top, r/ClaudeAI top, r/LocalLLaMA top, r/MachineLearning top

Auto-curated weekly by Claude Opus 4.7 from Apple ML research, Ben’s Bites, Cursor changelog, Don’t Worry About the Vase (Zvi), Exponential View (Azeem Azhar), GitHub: All-Hands-AI/OpenHands, GitHub: BerriAI/litellm, GitHub: anthropics/claude-code, GitHub: crewAIInc/crewAI, GitHub: ggml-org/llama.cpp, GitHub: huggingface/transformers, GitHub: langchain-ai/langchain, GitHub: langchain-ai/langgraph, GitHub: ollama/ollama, GitHub: sgl-project/sglang, GitHub: simonw/llm, GitHub: vllm-project/vllm, GitLab blog, Google DeepMind blog, Hugging Face blog, Import AI (Jack Clark), Interconnects (Nathan Lambert), JetBrains AI blog, LangChain blog, Latent Space, Lenny’s Newsletter, NVIDIA developer blog, Not Boring (Packy McCormick), OpenAI blog, SaaStr (Jason Lemkin), Simon Willison, TLDR AI, The Algorithmic Bridge (Alberto Romero), The Pragmatic Engineer (Gergely Orosz), Together AI blog, Tomasz Tunguz, Understanding AI (Timothy B. Lee), Vercel blog. Source list and editorial profile maintained by Daniel.