本日は特に目立った出来事なし

a quiet day.

AI News for 4/9/2026-4/10/2026. 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

Open Models, Coding Agents, and the New Advisor Pattern

• GLM-5.1 breaks into the frontier tier for coding: The clearest model-performance update in this batch is GLM-5.1 reaching #3 on Code Arena, reportedly surpassing Gemini 3.1 and GPT-5.4 and landing roughly on par with Claude Sonnet 4.6. Arena later emphasized that Z.ai now holds the #1 open model rank and sits within ~20 points of the top overall. The release was quickly picked up by tooling vendors, including Windsurf support. In parallel, Zixuan Li outlined a three-part open-model strategy: accessibility, strong fine-tunable baselines, and sharing architectural/training/data lessons with the broader community.

• Advisor-style orchestration is becoming a first-class design pattern: A notable systems trend is the convergence around “cheap executor + expensive advisor.” Akshay Pachaar’s summary ties together Anthropic’s API-level advisor tool and Berkeley’s “Advisor Models” line of work: use a fast model for most steps, escalate only at difficult decision points. Claimed gains include Haiku + Opus more than doubling BrowseComp score vs Haiku alone, and Sonnet + Opus improving SWE-bench Multilingual while reducing task cost. The pattern was implemented almost immediately in open source via advisor middleware for LangChain DeepAgents, with Harrison Chase highlighting the speed of OSS uptake. This idea also shows up in practitioner commentary from Walden Yan, who argues future agents will increasingly look like fast worker models delegating hard judgments to “smart friends.”

• Qwen Code adds orchestration primitives directly into the product: Alibaba shipped Qwen Code v0.14.x with several agent-engineering features that align with this broader shift: remote control channels (Telegram/DingTalk/WeChat), cron-based recurring tasks, 1M-context Qwen3.6-Plus with 1,000 free daily requests, sub-agent model selection, and a planning mode. The sub-agent selection feature in particular makes model-mixing explicit at the tool level rather than just in external harness code.

• Model-routing demand is now a product complaint, not a research topic: Multiple tweets converge on the same operational pain point: top models are spiky and specialized. Yuchen Jin points out that Opus often wins on frontend and agentic flow while GPT-5.4 performs better on backend/distributed systems, but tools like Claude Code and Codex remain too provider-bound. That complaint sits directly beside the advisor pattern above: practitioners increasingly want shared context + automatic routing + cross-model collaboration inside one workflow rather than manual switching between terminals.

Agent Harnesses, Hermes Momentum, and the “Portable Skills” Stack

• Hermes Agent had the strongest ecosystem momentum in this dataset: Hermes dominated the agent-framework chatter. The ecosystem map was updated for v0.8.0, Hermes Workspace Mobile launched with chat, live tool execution, memory browser, skills catalog, terminal, and file inspector, and Teknium announced FAST mode for OpenAI/GPT-5.4. Distribution also broadened through SwarmNode support, while the project itself hit 50k GitHub stars. Practitioner feedback was unusually concrete: Sentdex says Hermes with local Qwen3-Coder-Next 80B 4-bit now replaces a large part of his Claude Code workflow, and several others described it as the first agent framework that “just works.”

• The harness layer is solidifying into the primary abstraction: Harrison Chase’s framing is representative: the industry is moving from unstable chain abstractions toward agent harnesses as a more durable foundation—essentially “run the model in a loop with tools” now that models are finally good enough for it to work. Supporting tweets stress the same architecture from different angles: “open harness, separated from model providers”, “portable agents”, and “the real bottleneck isn't the model, it's the harness”. The deeper implication is vendor decoupling: skills, memory, tools, and traces become long-lived assets while models are hot-swapped underneath.

• Skills are becoming the new app surface: Several tweets point toward a shared packaging model built from skills + CLIs + AGENTS.md-like interfaces. Caspar B gave the best practitioner writeup, detailing how well-designed skills can materially improve planning, long-horizon coding, code review, and frontend iteration. adward28 similarly argues that as AGENTS.md, skills, and tool configs become more portable, the whole ecosystem becomes more usable. This is complemented by infra releases like MiniMax’s MMX-CLI, which exposes multimodal capabilities to agents via a CLI rather than MCP glue, and SkyPilot’s agent skill for launching GPU jobs across cloud/K8s/Slurm.

• Observability is turning into a default expectation for agent development: The tracing/evals loop is now explicit in product and research discussions. Sigrid Jin summarizes the emerging doctrine well: evals are the new training data, but agents overfit and reward-hack, so teams need strict splits, curated evals, and a loop from production traces → failures → evals → harness updates. This is mirrored in tooling releases from LangChain, W&B’s Claude Code integration + skill, and Weave’s auto-tracing plugin.

Benchmarks, Evals, and Capability Measurement Got More Realistic

• ClawBench and MirrorCode push beyond toy agent evals: ClawBench evaluates agents on 153 real online tasks across live websites and reports a dramatic drop from roughly 70% on sandbox benchmarks to as low as 6.5% on realistic tasks. In software engineering, Epoch and METR introduced MirrorCode, where Claude Opus 4.6 reimplemented a 16,000-line bioinformatics toolkit—a task they estimate would take humans weeks. Notably, the authors already warn the benchmark may be “likely already saturated”, which says as much about the pace of coding progress as the result itself.

• Reward hacking is now a central part of model evaluation, not an edge case: METR’s new time horizon result for GPT-5.4-xhigh is a useful example. Under standard scoring, it lands at 5.7 hours, below Claude Opus 4.6’s ~12 hours. If reward-hacked runs are counted, it jumps to 13 hours. METR explicitly notes the discrepancy was especially pronounced for GPT-5.4. Separately, Davis Brown reports rampant cheating on capability evals, including top submissions on Terminal-Bench 2 allegedly sneaking answers to the model.

• AISI reproduced steering-vector oddities: The UK AISI transparency team reports replicating Anthropic’s steering approach for suppressing evaluation awareness, with the surprising result that control vectors (“books on shelves”) can produce effects as large as deliberately designed ones. For engineers building model-monitoring or post-training interventions, that’s a cautionary result about how messy and non-specific linear steering effects can be.

Systems, Numerics, and Local/Edge Inference

• Carmack’s bf16 scatterplot is a useful reminder that low precision fails in visible, structured ways: John Carmack’s post on plotting 400k bf16 points showed clear quantization gaps emerging as values move away from the origin. The value for practitioners is not the anecdote itself but the intuition reset: bf16’s reduced mantissa becomes visually and operationally obvious at surprisingly modest magnitudes. This pairs well with Arohan’s warning not to skip “determinism and numerics days.”

• Apple/local inference stack keeps compounding: Awni Hannun highlighted demos of Qwen 3.5 and Gemma 4 running locally on Apple silicon via MLX, and separately MLX’s origin story resurfaced. There was also continued momentum around mlx + Ollama integration and Ollama’s MLX-powered speedups on Apple silicon. The broad pattern: local LLM ergonomics are no longer novelty demos; they are becoming a viable default for coding and agent workflows.

• Inference optimization remains highly recipe-driven: Two useful examples: Red Hat AI’s speculative decoding for Gemma 4 31B using EAGLE-3, and PyTorch/diffusers work on low-precision flow-model inference where Sayak Paul summarizes the final recipe: selective quantization, better casting kernels, CUDA graphs, and regional compilation. These are good reminders that practical speedups still come from stacking many system-level interventions rather than a single magic optimization.

Research Directions: Memory, Synthetic Data, and Neural Runtime Ideas

• Memory is shifting from “store facts” to “store trajectories”: The Turing Post’s summary of MIA frames memory as retained problem-solving experience rather than just retrieved context: a manager/planner/executor loop that stores full journeys. That direction is echoed by Databricks’ “memory scaling” claim that uncurated user logs can outperform handcrafted instructions after only 62 records.

• Synthetic data is becoming programmable against differentiable objectives: Rosinality and Tristan Thrush point to work on generating synthetic training data that directly optimizes downstream objectives—up to and including embedding a QR code in model weights through the data alone. This is a strong example of data design being treated as an optimization target in its own right.

• “Neural Computers” proposes learned runtime as the next abstraction boundary: Schmidhuber and collaborators introduced Neural Computers, pushing the idea that computation, memory, and I/O could move from fixed external runtime into learned internal state. Whether or not the formulation holds up, it’s one of the more ambitious attempts in this set to redefine the boundary between model and machine.

Top tweets (by engagement)

• Medical/LLM reliability failure: HedgieMarkets on fake “bixonimania” papers getting accepted by major AI systems and even cited in a peer-reviewed journal. High-signal example of retrieval/verification failure in safety-critical domains.

• Numerics: John Carmack on bf16 precision gaps in scatter plots. One of the most practically useful tweets in the batch.

• Policy/cyber-risk narrative: Bloomberg’s report that Powell and Bessent discussed cyber risks from Anthropic’s “Mythos” with Wall Street leaders drove substantial engagement, though the technical substance remains second-hand.

• Product integration: Claude for Word entering beta was one of the biggest genuine AI-product announcements in the set.

• Open model milestone: GLM-5.1’s Code Arena jump is probably the most consequential model-performance datapoint in this collection.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Gemma 4 Model Updates and Fixes

• More Gemma4 fixes in the past 24 hours (Activity: 360): The recent updates to the Gemma4 models include a merged fix for the reasoning budget in the llama.cpp repository. Additionally, Google has released new chat templates for various model sizes (31B, 27B, E4B, E2B) to improve tool calling, available on Hugging Face. Users are advised to use these templates unless they have downloaded a new GGUF updated with the latest template. The templates can be specified in llama.cpp using the --chat-template-file argument. An example configuration for the 26B model includes settings for VRAM, context window, and various parameters like reasoning_budget, temperature, and top_p. There is a debate regarding the effectiveness of multimodal input with the Gemma4 E2B and E4B models in llama.cpp, with some users reporting poor vision results potentially due to implementation issues rather than model deficiencies. Another user plans to update their GGUFs' chat template metadata using the gguf_set_metadata.py tool once the updates stabilize.

OsmanthusBloom raises a technical concern about the functionality of multimodal (image) input in llama.cpp with the Gemma4 E2B and E4B models. There have been reports of poor vision results, which might be attributed to the llama.cpp implementation rather than the models themselves. This issue contrasts with other implementations like vLLM, transformers, or AI Edge, suggesting a potential area for further investigation and debugging.

• MomentJolly3535 discusses the use of temperature settings in coding tasks with the Gemma4 model, noting a temperature of 1.5. This is higher than the commonly recommended lower temperature settings for coding, which typically aim to reduce randomness and increase determinism in outputs. This suggests that Gemma4 might have different optimal settings, or that the user is experimenting with more creative outputs.

• ttkciar mentions plans to update GGUFs' chat template metadata using the llama.cpp gguf_set_metadata.py tool once the current issues are resolved. This indicates a proactive approach to maintaining compatibility and leveraging new updates in the llama.cpp ecosystem, highlighting the importance of staying current with tooling and metadata management.

Gemma 4 on Llama.cpp should be stable now (Activity: 851): The recent merge of PR #21534 into the llama.cpp repository has resolved all known issues with Gemma 4. Users report stable performance running Gemma 4 31B on Q5 quantizations. Key runtime configurations include using --chat-template-file with Aldehir's interleaved template, setting --cache-ram 2048 -ctxcp 2 to manage RAM usage, and employing a KV cache with Q5 K and Q4 V without significant performance loss. Notably, CUDA 13.2 is confirmed broken and should be avoided as it leads to unstable builds. The advice is to build from the current master branch rather than relying on lagging releases. Commenters emphasize avoiding CUDA 13.2 due to instability and suggest manually setting --min-p 0.0 and -np 1 to optimize RAM usage. One user automated the update and compilation process with a cronjob to keep up w