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Learn languages by exploring worlds and talking to locals. Discussion | Link

When NASA’s Nancy Grace Roman Space Telescope launches, as early as the end of next month, it will attempt one of astronomy’s most precise disappearing acts to date. The telescope will carry the first space-bound “active” coronagraph, an instrument that effectively erases most of the light from a star during photography. It will allow astronomers…

Reviews drop today. This thread is for reactions, celebrations, commiserations, and anything useful in between. First: if you got good reviews, say so. There's a norm in these threads where only the bad news gets aired, and it skews everyone's sense of what's normal. Post your wins. Second , the thing worth repeating every cycle: the review process is noisy, and that noise is measured, not folklore. The NeurIPS consistency experiments (2014, repeated 2021) found that a large fraction of accepted papers would have been rejected by an independent second committee. Reviewer assignment, load, and luck of the draw account for a lot. A score is a weak signal about your work and a strong signal about the process that produced it. That cuts both ways. It's not a license to dismiss every criticism as noise — it's a reason to weight reviews by the quality of the argument rather than the number attached to them. The reviewer who found a real hole in your evaluation did you a favor, even if the tone was rough. The one who clearly skimmed did not, regardless of the score. So: prioritize the reviews that make the paper better. Fix what's fixable, contest what's genuinely wrong, and concede the rest gracefully in the rebuttal. Things worth discussing: Reviews that caught something you'd missed Rebuttal strategy — what's worth contesting vs. conceding, and when new experiments actually shift a score Patterns you're seeing this cycle (missing baselines, compute comparisons, ablation depth, reproducibility asks) Framing a response when a reviewer has clearly misread the submission Backup plans: ICLR, AISTATS, workshops Please paraphrase rather than paste review text, and no speculation about reviewer or AC identities. To anyone who got bad news: this doesn't define your research impact. Plenty of heavily-cited work took two or three cycles to land somewhere. Rejection is a scheduling problem. How did everyone do? submitted by /u/Afraid_Difference697 [link] [留言]

An AI workspace where your canvas and code work together. Discussion | Link

Synthesia launched AI Roleplay Sessions, an interactive enterprise training platform where employees practice workplace conversations with AI avatars that provide feedback, scoring, and analytics to help companies measure training effectiveness.

Google Cloud has published a new blueprint setting out how organisations should secure artificial intelligence workloads running on Google Kubernetes Engine, arguing that the shift from prototype to production has outpaced traditional security models. The document sets out a three layer approach covering infrastructure, model integrity and application security. By Matt Saunders

Build and send quotes as fast as you can talk Discussion | Link

Learn to fly with a live AI instructor Discussion | Link

A search engine with no ads Discussion | Link

Enterprise developer security control plane around evidence Discussion | Link

Paper: https://arxiv.org/abs/2607.19058 Code (GitHub): https://github.com/nuemaan/skewadam Hi everyone, I just published a preprint on a new optimizer designed to tackle the massive VRAM bottleneck in Mixture-of-Experts (MoE) training. If you've trained MoEs, you know that optimizer state is usually the largest single line item in the memory budget. AdamW, for example, spends 50.6 GB of state memory just to update a 12.6 GB model. I built SkewAdam to fix this by using a tiered state allocation . Instead of treating all parameters equally, it allocates precision based on parameter behavior: Backbone (5% of params): Momentum + Factored 2nd moment Experts (95% of params): Factored 2nd moment only Router (<0.01% of params): Exact 2nd moment The Hardware Results: Optimizer state memory drops from 50.6 GB to 1.29 GB (a 97.4% reduction). Peak training memory drops from 81.4 GB to 31.3 GB. This allows a 6.78B MoE to fit comfortably on a single 40GB GPU without sacrificing convergence or router stability. submitted by /u/Kooky-Ad-4124 [link] [留言]

What We Learned Building a Location-Aware Contact Management App Most contact apps are built like digital phonebooks. They store a name, phone number, email, maybe a company name, and then leave the user to remember everything else. That works when someone has 50 people saved. It starts breaking when someone has hundreds or thousands of professional connections from events, client meetings, referrals, business cards, conferences, online communities, and local networking groups. The hard part is not storing people. The hard part is helping users find the right person at the right time. While building a location-aware contact management app, we learned that contact data becomes far more useful when it is connected to context: where someone is, how the user met them, what they discussed, what industry they belong to, and why the relationship matters. Here are some product, UX, and privacy lessons we learned along the way. 1. A contact list is not the same as a usable network A normal contact list answers one basic question: “Do I have this person’s number?” But professionals usually need better questions answered: Who do I know in this city? Who did I meet at that event? Which industry contacts are nearby? Who should I follow up with before visiting this area? Who was that consultant I met last month? Which contacts are important but easy to forget? This is where the product problem becomes interesting. A user may technically have the contact, but still fail to use the relationship because the contact is buried inside a long list. So the first learning was simple: Saving contact details is not enough. The app needs to help users retrieve useful relationships when the context matters. That changed how we thought about the product. We were not just designing a place to store people. We were designing a system to make saved professional relationships easier to act on. 2. Location context changes the experience Most contact managers are list-first. You search by name, comp

I honestly thought learning REST APIs would be easy. At first, creating a simple GET or POST endpoint feels straightforward and you start thinking, "I've got this." Then reality hits. Every API needs middleware, validation, error handling, controllers, database integration, authentication, authorization, testing, pagination, CORS, environment variables, deployment and a dozen other things. Somewhere along the way, you realize you didn't just sign up to build an API—you signed up to build an entire backend ecosystem. 😂💻

When people hear about a new project scaffolding tool, one of the first questions they ask is: "Can I choose React Query or TanStack Query?" "What about pnpm instead of Bun?" "Can I use ESLint instead of Biome?" "Can I choose Radix instead of Base UI?" "Can I skip Tailwind?" These are reasonable questions. In fact, I asked myself the same ones while building create-notils . My first instinct was to make everything configurable. The more I thought about it, the more I realized I was about to build something I didn't actually want to use. The Configuration Trap Most project generators start simple. Then someone requests another option. Another package manager. Another ORM. Another authentication provider. Another CSS framework. Another UI library. Eventually the CLI starts looking like this: ? Which package manager? ❯ npm pnpm yarn bun ? Which CSS framework? ? Which ORM? ? Which auth library? ? Which formatter? ? Which icon library? ? Which deployment target? It feels flexible. But every new option creates more combinations to support. Five choices in one prompt don't create five possible projects. They multiply with every other prompt. The complexity grows much faster than the number of features. I Built the Tool I Wanted to Use One thing I've learned from building side projects is this: the first user should always be yourself. Every project I start today uses almost exactly the same stack: Next.js 16 React 19 Bun Tailwind CSS v4 shadcn/ui Base UI Biome TypeScript Turborepo (when needed) I wasn't switching between ten different combinations every week. I was rebuilding the same foundation over and over. So instead of asking twenty questions during scaffolding, I decided to optimize for the workflow I actually have. npx create-notils my-app A few seconds later, I'm writing features instead of answering prompts. Opinionated Doesn't Mean Closed There's an important distinction between opinionated and restrictive . Some tools hide their implementation behind abstraction

Originally published at tengli.dev Your MCP server can be 100% spec-compliant and still be unusable by an agent. The Model Context Protocol spec tells you how to transport tools: JSON-RPC framing, capability negotiation, schema shapes. It says nothing about whether a model can actually use what you serve — whether it picks the right tool out of your catalog, fills the arguments correctly, or burns 8k tokens parsing your schemas on every single request. I integrate first- and third-party MCP connectors into a production AI agent for a living, and I kept seeing the same failure: servers that pass every compliance check, yet the model calls the wrong tool, hallucinates arguments, or ignores the tool entirely. The problems were never in the protocol layer. They were in the parts no one lints: descriptions, naming, schema design. So I wrote mcpgrade — a Lighthouse-style scorecard for MCP servers. One command, no API key, report in seconds: npx mcpgrade --stdio "npx -y your-mcp-server" Then I pointed it at 36 popular servers. It did not go great. The results Full sortable table: https://tengli.dev/mcp-leaderboard.html . The short version (static analysis, point-in-time snapshot; servers marked (archived) are unmaintained reference implementations, included because they're still widely installed and copied): Top of the class (A): brave-search (archived) , exa, google-maps (archived) , slack (archived) , perplexity-ask, @shopify/dev-mcp, @apify/actors-mcp-server, airbnb, figma-developer-mcp, tavily, gitlab (archived) , elastic, shrimp-task-manager, and more — 15 of 36. Bottom of the class (D/F), 11 of 36 — and it's not hobby projects: MongoDB's official server (66, with 66 errors), Notion's official server (62), Airtable (69, 66 errors), todoist-mcp-server (67, 110 errors ), GitHub's archived reference server (67, 44 errors), and firecrawl-mcp at the very bottom (57, 134 errors ). Two more servers (Stripe, Supabase) couldn't be scanned with dummy credentials and were exclud