Updated
Updated
Showing low-relevance items too. Hide low-relevance items
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.
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 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.
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.
Hamel Husain@HamelHusainAI score25AIHamel Husain advises against building automated evaluators for every failure mode discovered during AI development. He argues that teams should weigh the costs of different evaluator types, such as code-based checks versus LLM judges, before building an eval.
merve@mervenoyannAI score4AIHugging Face's Merve Noyan says the company already offers four video series on YouTube for viewers who want to learn how to do it. She links to one playlist in the post, but the post does not specify which topic the series cover.
merve@mervenoyannAI score22AIHugging Face's Merve Noyan promoted a four-chapter YouTube series teaching viewers how to train AI agents using only open-source tools. The post links to the series' first video and is a brief call to learn agentic training.
Khazix@Khazix0918AI score13AIKhazix says he used AI to customize a pixel clock so it mirrors all Claude Code states on his computer, including parallel sessions. He adds that Claude produces far better visual design than Codex, which he finds ugly.
Allie K. Miller@alliekmillerAI score5AIAllie K. Miller argues that people can change how they use AI within one weekend by abandoning three habits: prompting, typing, and relying on a computer screen. The post offers no specific tools, products, or steps beyond these three directives.
AIPhysicist Matthew Schwartz describes building BootLoops, an open-source harness for exact quantitative calculations, after choosing problems suited to Claude's strengths. He reports that Claude solved long-standing integrals and found connections across ecology, population genetics, economics, and linguistics, with domain experts steering results toward questions those fields care about. The post states that the approach required constant human oversight, since Claude often overstated results and misjudged time.
Why it matters: The guest post explains why scientists often find current AI tools frustrating and offers a method for finding problems where AI and researchers match, backed by concrete projects.
AILangChain built a model router inside its open source coding agent Open SWE that picks one of three models for each thread. In an A/B test against always using GPT-6 Astra, the median cost per thread fell 64% with no measurable change in merged PR rate. The router runs on the thread's first message, using a base prompt, per-tier criteria, and a classifier model, and the post lists next steps including subagent routing and mid-thread re-routing.
Latent.Space@latentspacepodAI score67AILatent.Space shares a podcast episode in which OpenAI's AriX and Nikunj Handa discuss OpenAI's new agent stack. The episode covers Computer Use, Dots cloud computers for agents, and the Decisions API, which the speakers say went from idea to product in weeks.
Google Flow@FlowbyGoogleAI score38AIGoogle Flow publishes a guide to creative prompting with Gemini Omni Flash, covering video generation for films, marketing, and visual assets. The guide recommends high-level constraints, first and last frame visual anchors, tagged image, video, and storyboard ingredients, and granular mid-scene pacing edits. It also suggests transferring style and motion from reference images and videos.
Google Cloud Tech@GoogleCloudTechAI score28AITerminal test runs miss multi-turn agent regressions, so Agent Clinic Episode 3 builds an automated eval suite for a LangGraph agent in 60 minutes. The post presents a four-step framework for moving from informal checks to benchmarking AI agents, with a link to the full guide.
Google Cloud Tech@GoogleCloudTechAI score4AIGoogle Cloud Tech promotes a Medium article titled "How to run open models in the cloud without going broke." The post contains only a link and no further details about the methods, models, or costs discussed.
Google Cloud Tech@GoogleCloudTechAI score15AIGoogle Cloud recommends limiting accelerator count to a single GPU, setting replica count to 1-1, and avoiding capacity reservations to keep monthly bills predictable. These strict hardware limits apply to auto-scaling configurations for AI workloads.
Google Cloud Tech@GoogleCloudTechAI score7AIGoogle Cloud recommends Spot VMs for developer and agent testing workflows that tolerate interruptions and do not need continuous uptime. Spot VMs use spare Google Cloud compute capacity at deep discounts.
eric zakariasson@ericzakariassonAI score36AICursor's Eric Zakariasson shared a game-builder skill he has been using to build games with new models. The skill can be installed with the command npx skills add ericzakariasson/skills --skill game-builder.
NVIDIA AI@NVIDIAAIAI score40AINVIDIA says a single prompt can build and deploy a visual AI agent for a manufacturing line in under 30 minutes, with alerts, video search, and incident reports. The method uses the new Build Vision AI skill in NVIDIA VSS Blueprint 3.3, and a tutorial is available for readers who want to build one.
Ant Ling@AntLingAGIAI score38AIAnt Ling reports that its Ling-3.1-flash model completed a roughly 20-hour Rust port of a C image library. After a performance regression caused by busy-waiting workers and a parallelism adjustment, the model recovered and reached an 8.015× speedup. All 30 correctness checks passed.
NVIDIA AI@NVIDIAAIAI score27AINVIDIA and Nous Research published a hands-on walkthrough of NVIDIA NeMo Relay for collecting traces from Hermes Agent. The guide runs two example scenarios and shows the agent's calls and retries in Arize Phoenix. It also covers how Nous used traces and task results to evaluate fixes across repeated runs.
AIAgentic data science has AI agents explore datasets, choose modeling approaches, run analyses, and explain findings while data scientists frame questions and verify evidence. In an experiment, Claude Opus 5.0 given the vague prompt "Build me a model to detect fraudulent nodes" on a modified Elliptic Bitcoin dataset reported F1 0.87 and ROC AUC 0.99 using a random split that leaked a planted label proxy.
Ideogram@ideogram_aiAI score20AIIdeogram recommends restoring an old photo in stages: first remove scratches and damage, then add color, then enhance. The post says trying to fix everything in a single prompt rarely works, so multi-turn editing lets each edit be refined separately.
Hamel Husain@HamelHusainAI score15AIHamel Husain advises starting with broad exploration of production traces, then using signals to locate likely failures. He recommends keeping some random traces in every review batch so new problems can still be discovered.
Khazix@Khazix0918AI score9AIThe author, whose earlier device was flagged so the account got banned within about half an hour, reports a new Claude account has run stably for six days. The shared tips include logging in with a Google account, using a home static IP, a clean new device, timezone set to Taiwan, paying via Google Play, starting at the $20 Max tier, and running Claude on a single always-on Mac Mini accessed remotely.
howie.serious@howie_seriousAI score22AILocal and desktop agents such as Claude Code and Codex are powerful largely because they can use the shell, which connects them to the whole CLI ecosystem. The post lists tools including git, ffmpeg, curl, pandoc, gh, cron, and ssh as examples. It also says the video itself was produced by an agent operating the shell.
Karl's AI Watts@aiwartsAI score38AIKarl's AI Watts asks whether a menu-bar tool can switch models while preserving the existing conversation, so users avoid re-explaining their project each time. The post frames this as the reason they want to keep the menu bar tool, which the quoted post describes as magpie, a menu-bar switcher for 20+ agents including Claude Code and Codex that also offers a local gateway.
AIHamel Husain reviewed Anthropic's new build_eval and hill-climb commands in the claude-api plugin for Claude Code, finding it useful for discovering issues like human handoff, formatting, and voice agent problems. He criticized it for pushing evaluator creation before data review, asking for label validation in Markdown files, and bundling four failure checks into one broad call-transfer evaluator. Husain says he would hold off on using it for now.
GitHub@githubAI score13AIGitHub shared a video about the making of one of its most starred repositories. The post links to a YouTube video and gives no further details about the project or its development.
Julien Chaumond@julien_cAI score25AIJulien Chaumond, who owns Hugging Face, recommends a DGX Spark guide written by @0xSero and published on the Hugging Face blog. The post itself gives no further technical details about the handbook's contents.
Liquid AI@liquidaiAI score18AILiquid AI has published documentation for its decision models, including a models page, a migration guide, and a road-decider demo tutorial on GitHub. The post contains links only and gives no model names, parameter counts, benchmark scores, or pricing.
Amjad Masad@amasadAI score34AIAmjad Masad shared a post on designing and building a harness to reach frontier performance at a fraction of the cost. The post itself gives no specific models, benchmarks, or figures, and the linked article from @pirroh supplies the background context.
Google Gemma@googlegemmaAI score20AIGoogle Gemma says DiffusionGemma can be turned into a Jev-like model by seeding a canvas with a response template in vLLM. The approach extracts confidence scores and probability distributions for yes/no, multiple-choice, and scored questions in a single denoising step.