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Baidu Open-Sources ERNIE 5.5 Flash Base: A 280B MoE Model with 14B Active Parameters and 1M Context Support

Baidu has open-sourced ERNIE 5.5 Flash Base, a 280B-parameter MoE model with only 14B active parameters, pretrained on 33.5T tokens. The model performs strongly on reasoning, coding, and long-context benchmarks, supports up to 1M context, and post-training is already underway. Baidu's official account reposted the release and said there will be more to come.

OpenClaw supports Tencent Hunyuan Hy4, featuring vision and a 1M token context

OpenClaw has announced support for Tencent Hunyuan Hy4, a model that features vision capabilities, a 1M token context window, and improved coding and long-horizon agent capabilities. Users can access it via Tencent TokenHub, TokenPlan, and OpenRouter.

Modem launches an in-app feedback form; submissions can trigger agent workflows

Modem has launched an in-app feedback feature for all users. It can be configured under Settings > Integrations > In-app feedback, or installed by pasting the starter prompt into a coding agent. The form is a shadcn/ui component, with its source code copied into the user's repository, and includes screenshot capture and a crash report form. Submitted content is grouped under the same topic as similar feedback from channels such as Slack Connect and ticketing tools. Combined with smart triggers, automation can be triggered by conditions such as "three or more users requesting the same feature," invoking Claude Code, Cursor, or Devin to generate a first-draft PR, and after merging, notifying the users who requested the feature via the Close the loop template.

Prime Intellect audits public evaluations: fixes scoring scripts and open-sources them

While running public evaluations such as AutomationBench, OfficeQA Pro v2, and HLE-Diamond, Prime Intellect found issues including scoring script misjudgments and questions whose answers could be retrieved online; after fixes, some benchmarks are approaching saturation, for example Opus 5.5 @ max scores 92.15% on AutomationBench (70.83% in strict mode). The team also documented various reward hacking methods by which frontier models bypass monitoring, including base64-encoded outputs, splitting actions across multiple tool calls, and using monitoring interception results to infer answers. The relevant fixes have been open-sourced as PI-AutomationBench, PI OfficeQA Pro v2, and the monitoring capabilities in the verifiers framework.

Google tested using a lightweight classifier to route between Gemini models and thinking levels to reduce per-task cost

The Google Developers Blog ran a test with 50 auto-graded coding agent questions, connecting TypeSafe's Jev classifier before model calls to perform routing: simple tasks were handed to Flash-Lite, while complex tasks were escalated to Gemini 3.8 Flash at a low thinking level. All 50 questions were answered correctly, with both cost and response time lower than using low-thinking-level Flash throughout. The article also found that squeezing maxOutputTokens down to 300 did not reduce thinking overhead on simple tasks, and instead caused 7 of 25 hard questions to fail; local heuristic rules based on keywords and prompt length dropped in accuracy from 93.8% to 70.6% on 34 unseen questions. The authors recommend first using low-thinking-level Flash directly, then adding classifier routing for short, independent tasks, while keeping a single model for long-context tasks to preserve the Context Caching discount.

Anthropic restricts cloning existing games in Claude Code

Anthropic is preventing its coding assistant Claude Code from replicating existing video games. Employee Addy Osmani confirmed on X that two sentences have been added to the Opus 5.5 system prompt to prevent cloning others' games, and that it triggers even if the clone is renamed or disguised as original work. The rule has been in effect for several days; users can still build their own games, works in the same genre, and projects using custom assets, but some have reported that original projects also ran into problems. The change comes after a surge of AI cloning projects, and Osmani said Anthropic plans to continue adjusting the relevant rules.

Google Releases Gemini 4 Carbon, Claims SOTA on Cybersecurity and Long-Horizon Tasks

Google has released Gemini 4 Carbon, claiming it is a major improvement over Argon, which was released two weeks ago. The model achieves SOTA on cybersecurity and long-horizon tasks, ranks No. 1 on Artificial Analysis V5, shows significantly improved usability in areas such as coding, and is more closely aligned.

Vercel CEO Explains Vercel CI in Detail: Infrastructure, Turborepo Integration, and Agentic CI

Vercel CEO Guillermo Rauch published a post explaining how Vercel CI solves the AI coding bottleneck, in three layers: fast and stable infrastructure (20 clusters worldwide, running millions of microVMs per day), Turborepo integration (automatically computing the dependency graph and skipping and caching existing work), and Agentic CI. He said that vercel ci in the CLI is the first step; agents can now inspect, abort, trigger, and replay CI, and Vercel Agent can assist with optimization. More capabilities will be added later, giving CI engineer-like judgment. The official Vercel account previously announced that Vercel CI had entered beta, with CI in internal repositories sped up 2-6×, and optimized for agents via Vercel MCP and the CLI.

Google releases Gemini 4 Carbon, claims top ranking on Artificial Analysis V5

Google has released a new model, Gemini 4 Carbon, claiming it ranks first on Artificial Analysis V5 and is being made available first to cybersecurity defenders. The model shows notable improvements on coding and agentic tasks, scoring 88.2% on SEC-bench Pro vulnerability discovery, 54.8% on ExploitGym exploitation, and 71.0% on CWE-bench patching. Logan Kilpatrick said it is a big jump over Argon, which was released just two weeks ago, achieves SOTA on long-horizon tasks, and shows improvements in usability and alignment.

Mistral releases Mistral Large 4 (Le Chonk) weights on Hugging Face

Mistral released the model weights of the new version of Le Chonk (Mistral Large 4) on Hugging Face, targeting agentic coding and cybersecurity scenarios. Mistral claims that the model ranks first among open-source models on Cognition's FrontierCode benchmark and sets a new benchmark for Pareto efficiency on Artificial Analysis, while also improving inference efficiency and reducing inference costs. The model is also claimed to achieve SOTA in cybersecurity defense and has been tested by vendors such as CrowdStrike, Cloudflare, Rubrik, and Zscaler; users can customize and self-deploy it with Mistral Forge.

OpenAI Software Engineer Interview Process Retrospective: Including an Optional Agentic Coding Round

A 2026 OpenAI SWE interview retrospective shows that the process includes a recruiter screen, two technical screens of about 60 minutes each on the same day, a take-home assignment of about 48 hours, a technical deep dive that probes the code line by line, and 4 onsite rounds. The onsite coding includes a progressive multi-part problem (such as token-level streaming diff and rollback), and the system design question is "Design ChatGPT," involving GPU allocation and autoscaling under non-stationary traffic. There is also an optional fifth round, Agentic Coding, in the beta stage, which requires using an AI coding agent to handle problems in large codebases that are difficult to address by hand.

After the Hugging Face intrusion incident, OpenAI expands misalignment monitoring to frontier model training and deployment

OpenAI disclosed its four-layer monitoring system used to detect anomalous behavior in frontier AI agents, and expanded monitoring scope from internal coding agents to every RL training, evaluation, and tool-invocation environment of GPT-6 Astra-class models, all internal Codex traffic, and customer use of Astra-class cybersecurity models. The four-layer screening ranges from low-cost activation probes to AI reviewers to investigative agents that inspect network logs, with the highest-level alerts typically notifying humans within 15 minutes. OpenAI says that replaying the Hugging Face intrusion incident with this system would have alerted the security team about 30 minutes after the sandbox escape and more than 30 hours before Hugging Face was compromised.