本日は特に目立った出来事なし

a quiet day.

AI News for 5/12/2026-5/13/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews' website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Agent Infrastructure, Harnesses, and Developer Platforms

• Cline, LangChain, Notion, and Cursor all pushed deeper into agent platform territory: Cline open-sourced a rebuilt Cline SDK and refreshed CLI with a TUI, agent teams, scheduled jobs, and connectors, positioning its harness as a reusable substrate for custom coding agents. LangChain shipped a large batch of agent lifecycle infrastructure at Interrupt: LangSmith Engine, SmithDB, Sandboxes, Managed Deep Agents, LLM Gateway, Context Hub, and Deep Agents 0.6. The most technically notable piece is SmithDB, a purpose-built observability database for nested, long-running traces with large payloads, reportedly yielding 12–15× faster access on key workloads; the team says it is built atop Apache DataFusion and Vortex. In parallel, Notion’s External Agents API lets third-party agents such as Claude, Codex, Cursor, Decagon, Warp, and Devin operate directly inside Notion as a shared, reviewable context layer rather than another silo. Cursor expanded cloud agents with fully configured development environments including cloned repos, dependencies, version history, rollback, scoped egress, and isolated secrets.

• Agent UX is increasingly about long-running state, streaming, and orchestration rather than chat: Several launches converged on the same design direction. Duet Agent proposes a state-machine harness for jobs that last weeks or months, with parent/sub-agent coordination and memory replacing compaction. LangChain’s OSS updates added streaming typed projections, checkpoint storage, code interpreter, harness profiles, and model-specific tuning, all aimed at richer agent event streams than plain tokens. Tabracadabra moved from autocomplete to a context-aware assistant in any textbox, while VS Code introduced an Agents window and better multi-project task review. The architectural message across these releases is that production agents increasingly need durable execution, inspectable intermediate state, and tool-native UI surfaces rather than stateless prompt/response loops.

Model Training, Architecture, and Data Efficiency

• Pretraining efficiency and architectural experimentation were the strongest research throughline: Nous Research’s Token Superposition Training modifies the early phase of pretraining so the model reads/predicts contiguous bags of tokens before reverting to standard next-token prediction; they report 2–3× wall-clock speedup at matched FLOPs with no inference-time architecture change, validated from 270M to 3B dense and 10B-A1B MoE. Jonas Geiping et al. argued current message-based/chat training overly constrains agents to a single stream and released a multi-stream LLM paper claiming lower latency, cleaner separation of concerns, and more legible parallel reasoning/tool use; paper and code are linked here. δ-mem proposed an external online associative memory attached to a frozen full-attention backbone, with an 8×8 state reportedly improving average score by 1.10× and beating non-δ-mem baselines by 1.15×, with larger gains on memory-heavy benchmarks.

• Post-training/compression and data curation also produced notable results: NVIDIA’s Star Elastic claims one post-training run can derive a family of reasoning model sizes, at 360× lower cost than pretraining a family and 7× better than SOTA compression. Datology’s VLM work, highlighted by Siddharth Joshi and Pratyush Maini, argues data curation alone can produce major multimodal gains: +11.7 points across 20 public VLM benchmarks at 2B, beating InternVL3.5-2B by roughly 10 points at about 17× less training compute, and near-frontier 4B performance with 3.3× lower response FLOPs than Qwen3-VL-4B. On the open data side, Percy Liang said the next Marin run already has 18T tokens in its mix and is still seeking more pretraining, mid-training, and SFT data, with a companion token viewer shared here.

• Open evaluation and dataset work is maturing alongside model building: Kevin Li’s SWE-ZERO-12M-trajectories is positioned as the largest open agentic trace dataset: 112B tokens, 12M trajectories, 122K PRs, 3K repos, 16 languages. Victor Mustar flagged llama-eval as a step toward more comparable llama.cpp community evals. Meanwhile, Steve Rabinovich and Sayash Kapoor argued credible agent evaluation requires log analysis, not outcome-only metrics, because stronger agents expose hidden benchmark bugs and reward-hacking paths.

Enterprise AI Pricing, Platform Competition, and Distribution

• Anthropic vs OpenAI competition sharpened around enterprise distribution and developer lock-in: Ramp data cited by Andrew Curran showed Anthropic at 34.4% of businesses vs OpenAI at 32.3% in April, the first apparent lead change in business adoption; The Rundown amplified the same figures. At the same time, Anthropic changed plan economics: ClaudeDevs announced that paid Claude plans will get a dedicated monthly credit for programmatic usage across the Agent SDK, claude -p, GitHub Actions, and third-party SDK apps. This was immediately read by power users as a major restriction on subscription-subsidized harnesses, with criticism from Theo, Jeremy Howard, Matt Pocock, and Omar Sanseviero. Anthropic partially offset that backlash with a separate 50% increase in Claude Code weekly limits through July 13, stacked on the previously announced 2× 5-hour limit increase.

• OpenAI responded aggressively with Codex enterprise incentives: OpenAI Devs and Sam Altman offered two months of free Codex usage for enterprise customers switching in the next 30 days. OpenAI also published more technical platform detail, including a Windows sandbox design write-up describing the combination of local users, firewall rules, ACLs, write-restricted tokens, DPAPI, and helper executables needed to safely run coding agents with local filesystem/tool access. The competitive dynamic now looks less like “best model wins” and more like subsidy + workflow control + harness compatibility.

• Enterprise adoption is increasingly tied to runtime/security assurances: Perplexity described a hardware-isolated sandbox architecture with VPC-level separation, short-lived proxy tokens, and scanning of external content before agent actions, with additional details on encryption and auto-deletion. Aravind Srinivas framed this as foundational to Perplexity becoming an enterprise knowledge/research platform. The broader pattern: agent vendors are no longer selling only intelligence; they’re selling bounded execution environments.

Autonomous Science, Cyber Capability, and Robotics

• Recursive self-improvement moved from idea to startup cluster: The largest single meta-theme was the launch of Recursive, founded to build AI that automates science and safely improves itself. Launch posts from Richard Socher, Josh Tobin, Dominik Schmidt, Jenny Zhang, and Shengran Hu suggest a team drawn from open-endedness, AI Scientist, and research automation work. In adjacent work, Adaption’s AutoScientist aims to automate the full training-research loop outside frontier labs, with Sarah Hooker arguing that most model training failures are due to research-loop brittleness rather than mere compute scarcity.

• Cyber capability evaluations continue to steepen: The UK AI Security Institute said the length of cyber tasks frontier models can complete has been doubling every few months, and that recent models are beating prior trends. Anthropic/Glasswing’s Logan Graham said Claude Mythos Preview is the first model to solve both AISI end-to-end cyber ranges, including Cooling Tower, and the only one to clear every task under the institute’s 2.5M-token cap. XBOW reportedly found “token-for-token, unprecedented precision,” and partner usage allegedly surfaced thousands of high/critical vulnerabilities in weeks. Independent commentary from scaling01 claimed a newer Mythos version completed a cyber range 6/10 times vs 3/10 for the preview baseline.

• Robotics got a concrete long-horizon deployment demo: Figure’s Brett Adcock streamed humanoid robots running a full 8-hour autonomous shift on package sorting using Helix-02, with follow-up details that the robots reason from camera pixels, operate around human parity (~3s/package), perform on-device inference, coordinate as a networked fleet, autonomously swap for low battery, and self-diagnose/fail over to maintenance when needed here. This is one of the clearer public demonstrations of multi-robot, long-duration, no-human-in-the-loop orchestration rather than a short benchmark clip.

Top tweets (by engagement)

• Claude Code pricing and limits: @ClaudeDevs on 50% higher weekly limits, @ClaudeDevs on programmatic credits, and the ensuing developer backlash from @theo made pricing policy the day’s most consequential developer story.

• Codex enterprise push: @sama offering two free months of Codex usage for switchers and @OpenAIDevs’ enterprise call-to-action signaled an unusually direct go-to-market counterpunch.

• Figure’s 8-hour humanoid shift: @adcock_brett’s livestream post drew enormous attention and is one of the few viral posts in the set with clear technical substance.

• Cline SDK launch: @cline’s SDK release was one of the highest-engagement genuinely technical launches, reflecting demand for open coding-agent harnesses.

• Token Superposition Training: @NousResearch’s TST post stood out as a rare pretraining-method tweet that broke through widely, likely because the claim—2–3× training speedup without changing inference-time architecture—is concrete and economically important.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Efficient On-Device LLM Inference

• Needle: We Distilled Gemini Tool Calling Into a 26M Model (Activity: 451): Cactus Compute open-sourced Needle, a 26M-parameter single-shot function/tool-calling model using a “Simple Attention Network” architecture—attention + gating with no FFNs/MLPs—arguing tool use is mainly retrieval/slot extraction/JSON assembly rather than deep reasoning. It was pretrained on 200B tokens over 16 TPU v6e in 27h, post-trained on 2B Gemini-synthesized function-calling tokens in 45m, claims 6000 tok/s prefill and 1200 tok/s decode on consumer devices, and reportedly beats FunctionGemma-270M, Qwen-0.6B, Granite-350M, and LFM2.5-350M on single-shot function calling; code/weights are MIT-licensed on GitHub, Hugging Face, with architecture notes in the SAN writeup. Commenters framed Needle as potentially useful as a lightweight router that selects tools or dispatches queries to larger LLMs with parameters, while questioning whether the same no-FFN/cross-attention approach could generalize to summarization. One technical caution noted the repository apparently includes Python pickle files, which are discouraged due to code-execution/security risks and Python-specific portability issues.

Several commenters focused on the architectural implication of a 26M distilled tool-calling model as a lightweight router: it could classify/route requests to the appropriate larger LLM, tool, or RAG pipeline with the right parameters, rather than generating full answers itself. One suggested this could be extended into a small post-trained model that consumes structured RAG output and verbalizes it in natural language.

• A technical point was raised around the claimed “no FFN” result: if external structured knowledge is always supplied via tools/RAG/retrieval, the model may not need FFN layers to store factual knowledge in weights. This implies a possible design pattern where compact attention-heavy models specialize in orchestration or grounding over provided context instead of memorization.

• One commenter noted that publishing pickle files is increasingly uncommon because of Python-specific dependency coupling and arbitrary-code-execution risks during deserialization. Another highlighted that Gemini has had visible tool-calling quirks, including system-prompt-level patches around tool specificity and avoiding inefficient file operations like cat in favor of dedicated tools such as grep_search, which could matter if Gemini-generated traces were used as distillation data.

I got a real transformer language model running locally on a stock Game Boy Color! (Activity: 1326): The image shows a stock Game Boy Color running a local transformer demo labeled TINYSTORIES Q8 GBC, matching the post’s claim that Andrej Karpathy’s TinyStories-260K was converted to INT8/fixed-point and executed directly on-device without PC, Wi‑Fi, link cable, or cloud inference: image. The project uses GBDK-2020, an MBC5 Game Boy ROM, bank-switched cartridge ROM for weights, cartridge SRAM for the KV cache, and on-device tokenization/prompt entry; the author notes generation is *extremely slow* and mostly gibberish due to heavy quantization/approximation, but the transformer prefill + autoregressive loop works. Source code: github.com/maddiedreese/gbc-transformer. Comments are mostly impressed rather than technical, framing it as an impractical but compelling proof-of-concept—e.g. *“Pointless. Therefore, indispensable.”* and interest in porting similar experiments to