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NVIDIA AI@NVIDIAAIAI score40AINVIDIA reports that fine-tuning Nemotron 3.5 ASR reduced word error rate on Najdi and Hijazi Saudi Arabic from 55% to 30%. The post says a new tutorial shows how to adapt the model for other dialects and languages.
Together AI@togethercomputeAI score34AITogether AI's CPO and product team outlined how they use AI to make the whole team more productive, not just individuals. The approach includes a shared context repo readable by any AI harness, cutting half a day of research to about 5 minutes, and evals that test their product the way agents actually use it.
PixVerse@PixVerseAI score16AIPixVerse announced a plugin that lets users generate an unboxing video from a product description, setting, and presenter reaction. According to the post, the AI agent uses PixVerse to produce the video.
Google Cloud Tech@GoogleCloudTechAI score14AIGoogle Cloud's DevEx program sprint tested agent governance on Gemini Enterprise using standard external accounts and no internal shortcuts. The goal was to identify and fix developer friction in building enterprise AI.
Hamel Husain@HamelHusainAI score12AIHamel Husain recommends binary pass/fail labels over 1-5 Likert ratings when evaluating AI systems. He argues that binary labels force clearer thinking, produce more consistent labeling, and are significantly less complex to operationalize.
AIAirbnb CTO Ahmad Al-Dahle, who joined from Meta in January, says 60% of the company's code is now AI-authored and pull-request throughput per engineer is up about 1.6x. Roughly half of Airbnb's support tickets are now resolved purely by AI, which the company tested with synthetic data before production. Airbnb's internal context graph Everest helped speed up the grocery delivery and airport pickup services, which took eight to nine months and about six weeks to build, respectively.
Krea@krea_aiAI score18AIThe team at @thesystms built a creative agent in 48 hours, and @ingi_erlingsson documented how he reads a moodboard, starting with feeling, then color, tone, and camera. @armandgw turned that process into a Krea Agent skill.
AIArchitectural Decision Records (ADRs) give coding agents durable project context, helping them distinguish intentional decisions from implementation details. Agents can over-apply accepted but obsolete ADRs, so the author recommends explicit AGENTS.md instructions treating accepted ADRs as binding, prompting agents to flag conflicts, and keeping each ADR current rather than recording amendment logs.
Khazix@Khazix0918AI score18AIUsing an AI agent, the author rewrote a desk hardware pixel clock in Rust and linked it to the working status of both Claude and Codex agents. The device also monitors quota resets in real time and shows the day's token consumption. The quoted post notes the project ties into Claude Code's session state with parallel-session support, and says Claude's visual design was far stronger than Codex's.
Dongxi NLP@dongxi_nlpAI score22AIDongxi NLP (@dongxi_nlp) says it followed a suggestion from @karpathy to write the ASD-STE100 controlled English standard into a system prompt. The post is brief and gives no benchmark results, prompt text, or test details.
AIServiceNow CoreAI introduced AutoSynthData, which uses a target model's failures and a stronger teacher's successes to generate and validate new agent training tasks. In EnterpriseOps Gym experiments, the Hybrid domain produced 2,000 samples and raised Gemma-4-26B-A4B-it mean Pass@1 by 7.2 percentage points, while the ITSM domain produced 1,994 samples and raised it from 18.77% to 27.18%.
Why it matters: The post shows how failure analysis, teacher demonstrations, and verifier checks combine into a repeatable pipeline for generating targeted agent training data.
AIEvery's senior editor describes moving from single-thread chatbot prompting to delegating complex projects to teams of coordinating subagents, using skills, orchestrator threads, context packets, MCPs, and computer use. He says a subagent workflow verified employee equity costs across multiple grants, strike prices, and vesting schedules, and returned a draft Slack message for approval. The shift was prompted by a June tweet in which Codex placed a colleague at Level 5 of the "Eight Levels of AI Adoption" framework.
AICreative director Johnson Sheng tested Kling 4.0 for commercial video production, focusing on stability during fast camera moves and dynamic action. He reports stable motion in whip pan and handheld push-in shots, a 30-second single-take fight scene, and consistent props and characters across scene changes. The post also covers performance and emotion control through prompt adjustments and multilingual generation. Kling states Kling 4.0 is in closed beta with an official launch planned for October, supporting up to 4K resolution and 10-bit HDR output.
howie.serious@howie_seriousAI score57AIClaude Code can now be modded to change how it behaves, customize its UI, and add custom features. Mods are written in a few lines of TypeScript or generated by Claude, and ship inside plugins installed with /plugin in the CLI or desktop app.
Andrej Karpathy@karpathyAI score38AIAndrej Karpathy says people will spend more time understanding language model outputs and suggests better formats than plain text. He recommends asking for ASD-STE100 controlled-language explanations, diagrams, interactive HTML pages, or custom explainer videos, and he is most bullish on the video format.
AIThe article compares LangChain/LangGraph and CrewAI workflow orchestration with OpenRouter's native model and provider routing. It says OpenRouter's models parameter provides an ordered, error-driven fallback list, while LangGraph and CrewAI handle state, memory, and delegation. Frameworks can also run on OpenRouter as the model layer underneath.
AITool definitions and tool-call responses differ between OpenAI, Anthropic, and Google, so a tool that works on one model may fail on another. The article compares six agent frameworks, including LangChain, CrewAI, and the OpenAI Agents SDK, by where each performs schema translation. It also describes OpenRouter's API-layer normalization, which accepts an OpenAI-style tools array and returns a standard tool_calls response for tool-capable models.
TypeSafe AI@typesafeaiAI score16AIA post from TypeSafe AI promotes Jev, a tool it says gives AI bots a way to listen. The quoted post from @SoCalJayF describes using Jev as a semantic VAD in a real-time voice AI, combining the live transcript and recent conversation to judge whether a user has finished speaking, including through hesitations and pauses. The integration was built with Agora ConvoAI.
Sophia Yang@sophiamyangAI score38AIFireworks reports that numerical mismatch between training and rollout engines can destabilize reinforcement learning, with a GLM 5.2 experiment showing collapsing reward without alignment and stable reward with it over 25 steps. The post notes that MoE models add further alignment challenges, as Qwen3.5-MoE differences in expert output combination caused disagreement even when one implementation used higher precision. Fireworks says it co-develops its trainer and rollout engine to keep frontier RL training aligned across numerics, kernels, and MoEs.
LiveKit@livekitAI score13AILiveKit has released documentation for a speech-to-text (STT) plugin that connects its agents framework to Microsoft AI. The post shares only a link to the guide and provides no further technical details, model names, or pricing.
Fireworks@FireworksAI_HQAI score13AIRollouts account for most of RL's compute cost, and splitting them from training across separate engines can introduce numerical mismatches. In MoE models, such mismatches can even route tokens to different experts. Fireworks says it co-builds both engines so training stays fast and consistent.
ComfyUI@ComfyUIAI score14AIComfyUI says a detailed behind-the-scenes article covers its story with the artist, the creation ideas, and the production process for the Yui project. The post links to the full write-up on the Comfy blog and points readers to the Comfy Agent page.
ComfyUI@ComfyUIAI score14AIComfyUI posts a behind-the-scenes video showing how the YUI project was produced. Per a quoted post from @8co28, Comfy Agent handled repetitive work such as model comparisons, quality checks, and shot regeneration, letting the creator focus on review and direction.
Replit ⠕@ReplitAI score4AIReplit says users can ask its AI to analyze signup funnel data and identify where to increase activation, then follow up by requesting a dashboard. The post frames this as a way to reach a first win without knowing the final output in advance.
Boris Cherny@bchernyAI score54AIClaude Code now supports mods that change its behavior, customize the UI, or add features, written in a few lines of TypeScript or generated by Claude. Mods ship inside plugins and are installed with /plugin in the CLI or desktop app, and the author says mods can be shared so others can try them.
Latent.Space@latentspacepodAI score55AIAnthropic's Thariq describes Claude Code mods, which can read conversation scope such as turn count and token usage. Mods run in process, so they can spawn subagents, parse their results with structured output, and modify the UI, which hooks cannot do. Mods ship inside plugins and are installed with /plugin in the CLI or desktop app.
TypeSafe AI@typesafeaiAI score20AIDSPy's official account highlighted a recorded office hours session showcasing Jev use cases and explainers. The quoted context says the session covered DSPy's Jev/System One implementation, the ReAnchor optimizer, and design patterns for selective compaction, tool approvals, and subagent delegation.
AIArtist 852 Hakoniwa made YUI, described as the first animated short created with Comfy Agent, which the ComfyUI team says took three days of focused work by one person at about 200,000 yen in total cost, excluding labor. The source says the film was made mostly with Seedance 2.5 and the making-of video with MiniMax H3, with Comfy Agent used to regenerate shots and compare video models.
TypeSafe AI@typesafeaiAI score34AIIn a business risk monitoring test by @edwardirby, the Jev judge matched the report quality of an ordinary LLM judge while missing no investigations, versus the LLM missing 5 of 11. The LLM's threat scores also flip-flopped from 0.35 to 0.68 to 0.50 on the same threat, while Jev was 250x cheaper and 3-6x faster.
Perplexity Developers@perplexitydevsAI score22AIPerplexity's developer account says pplx-decider-v1-27b can triage customer support tickets, with escalations routed to the Agent API. The cookbook example uses the Decisions API for the initial decision and the Agent API for escalated requests.
ClaudeDevs@ClaudeDevsAI score22AIAnthropic's ClaudeDevs account says its team used mods to build features in Claude Code, including the /diff command and AGENTS.md support. The post links to a blog article on Claude Code mods and invites developers to try them.
ClaudeDevs@ClaudeDevsAI score29AIAnthropic is adding pre-built sample mods to the Claude Code Playground repo for developers to adapt. The post points readers to a getting started guide on creating Claude Code mods.
ClaudeDevs@ClaudeDevsAI score20AIAnthropic's ClaudeDevs advises keeping Claude Code mods private or submitting useful ones to the Claude directory. Mods run with the same machine access as Claude Code itself, so users should install only mods from trusted sources.
Harrison Chase@hwchase17AI score22AIHarrison Chase says model routing is a provocative term that lacks a clear definition, but offers a practical approach. His four steps are to understand tasks, understand the models, build the router inside the harness, and track outcomes, aiming to lower costs without a performance hit.
Google Workspace@GoogleWorkspaceAI score32AIGoogle Workspace says Sheets canvas can turn static spreadsheet data into interactive tools such as Kanban boards, dashboards, and visual workflows from a simple prompt. Derek Snyder, Director of Product Marketing for Google Workspace, demonstrates the feature in the latest AI Boost Bite video.
Prime Intellect@PrimeIntellectAI score12AIExtropic designed the tasks and reward, while Prime Intellect supplied the RL infrastructure, including verifiers for environment construction, Hosted Training for the RL loop, and Prime Sandboxes for executing model code. Prime Inference serves the LLM judge and frontier baselines in the same workflow.
Prime Intellect@PrimeIntellectAI score32AIExtropic post-trained Qwen3.6-35B-A3B with Prime Intellect for thermodynamic ML research, nearly tripling its held-out eval results in about 100 GRPO steps. The team built a custom RL environment with verifiers and trained on Hosted Training, Prime Sandboxes, and Prime Inference. This let Extropic avoid managing multi-node GPU infrastructure and focus on research.
Zyphra@ZyphraAIAI score26AIZyphra published a paper and blog explaining how local mixing encodes relative position in global NoPE attention models. The work, titled "How Local Mixing Encodes Relative Position in Global NoPE Attention," is available on arXiv and on Zyphra's website.
Lewis Tunstall @ COLM 🌉@_lewtunAI score44AIHugging Face shows that training LFM2.5-2.6B with RL inside the agent harnesses themselves lifted held-out task success from 42% to 54% across four harnesses. Before training, the model solved 62% of tasks in Mini-SWE-Agent but only 33% in Claude Code, so the same model behaved very differently per harness. The approach uses an OpenEnv capture proxy to record tokens and logprobs, Harbor for tasks and sandboxes, and TRL's async GRPO trainer, with 31% fewer tool calls on already-solved tasks; training in OpenCode alone mostly improved OpenCode.
NVIDIA AI@NVIDIAAIAI score23AINVIDIA announces that developers can build visual AI agents from a prompt using NVIDIA Cosmos and VSS 3.3. The post is a broadcast announcement that links to a live stream and provides no further technical details.