Instantly

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

TLA-MCP: Quick follow-up to last week's announcement

TLA+ language - Tuple-binding destructuring everywhere a binder used to work — quantifiers, comprehensions, CHOOSE, function defs, with nesting: \E > \in Pairs : P(a, b) {a + b : > \in Pairs} - Unbounded CHOOSE now handles x = e in addition to the existing x \notin S pattern. Observability - Per-action transition counts in every check_spec response, sorted descending. Tells you instantly which disjunct is driving state-space cost. - Pre-flight advisories when max_depth > 100 or max_states > 1_000_000. - Tool descriptions now flag bounded vs. unbounded TypeOK and explain max_seconds is a soft bound checked between states. Repo: https://github.com/fabracht/tla-rs submitted by /u/Anxious_Tool [link] [comments]

Wix cutting

Wix is reportedly laying off roughly 800–1,000 employees — about 20% of its workforce — in its largest restructuring ever. The interesting part isn’t just the layoffs. It’s what they reveal about the economics of AI-first software companies. Wix’s core business is still growing: • Revenue reportedly rose ~14% YoY in Q1 2026 • Bookings were up ~15% • New AI-driven cohorts showed even faster growth But growth alone no longer protects margins when AI infrastructure costs explode. The pressure points: • Heavy investment in Base44, the vibe-coding startup Wix acquired in 2025 • Building and running proprietary AI models • Massive compute/inference costs • Expensive customer acquisition and marketing campaigns • A controversial $1.6B share buyback executed before the downturn At the same time, investors are questioning whether traditional website builders are becoming commoditized by AI. The bigger story is “vibe coding.” Users can now describe an app or website in plain English: “Create a sleek portfolio site with dark mode, payments, and a booking form.” AI generates the product instantly. That changes the value chain. The old moat was: templates + drag-and-drop builders. The new moat is becoming: AI orchestration + hosting + payments + integrations + reliability + distribution. Wix understands this. Instead of resisting the shift, they’ve aggressively moved toward it: • Acquired Base44 • Launched Wix Harmony, an AI-native creation platform • Combined natural-language generation with traditional visual editing • Pushed deeper into AI infrastructure and automation The irony is that AI didn’t kill Wix’s market overnight. It forced Wix to reinvent what “website building” even means. Pure AI tools can generate impressive demos quickly. But production systems still require: • uptime • commerce infrastructure • SEO • analytics • security • scalability • customer support That’s where incumbents still have leverage. This looks less like “AI destroyed Wix” and more like: a profitable software company being forced through an AI-era reset where efficiency, infrastructure costs, and platform strategy suddenly matter more than headcount growth. The broader lesson: AI is compressing the value of interfaces while increasing the value of infrastructure and distribution. The companies that survive won’t necessarily be the ones with the best demos. They’ll be the ones that can combine: • AI generation • operational reliability • ecosystem lock-in • cost control • and real business workflows AI is making software creation easier. But it’s also making software businesses much harder to defend. submitted by /u/Annual_Judge_7272 [link] [comments]

Claude Code keeps looping on the same fix

I watched Karpathy drop a CLAUDE.md post about session drift. The assistant kept suggesting a refactor I had already undone. I hit the same wall every time I opened a new Claude Code window. The token meter spiked to 163,122 raw tokens on a 87-file repo. I cut that to 17,722 after I changed the context handling. That is a 89.1% reduction. The difference showed up in the cost tab instantly. I built a tiny layer that injects bi-temporal hooks on Edit, Write, and Bash. The hooks fire before the tool runs, pulling the exact file versions you need. The layer also mines git revert commits during indexing, so it never suggests a change you already rolled back. Halfway through I added the package. Engramx by Cirvgreen drops in with a single npx command. It auto-installs six sentinel hooks and emits an AGENTS.md for any non-Claude client. No cloud calls. All data stays in a local SQLite. I tested 1,025 engramx by Cirvgreen unit tests and 36 skill-pack tests. Every test passed. The benchmark lives in bench/real-world.ts and can be run against any repo. If you run the demo you'll see the token graph flatten. The repo rebuilds in seconds instead of minutes. Apache 2.0. Local. Free. https://github.com/NickCirv/engram submitted by /u/SearchFlashy9801 [link] [comments]

at what point do ai-generated images stop feeling ai-generated?

a few years ago it was easy to spot ai art instantly now some generated images look almost indistinguishable from professional photography or digital art. where do you think the line between real and generated starts to disappear? submitted by /u/salarshah-084 [link] [comments]

I built a Chrome extension to navigate long ChatGPT conversations more easily

Hey everyone, I built a small Chrome/Chromium extension called ChronoChat to solve a problem I kept running into: long ChatGPT conversations becoming almost impossible to navigate. ChronoChat adds a sidebar to ChatGPT that turns the current conversation into a searchable map. You can: jump directly to specific messages filter by user or assistant turns search inside the current conversation use keyboard shortcuts for faster navigation export the full conversation as JSON, CSV, Markdown or PDF The extension runs locally in the browser. There is no backend, no analytics, and no remote runtime assets. I made it because I often use ChatGPT for research, coding, planning, and long iterative work, and scrolling through huge threads gets painful fast. GitHub repo: https://github.com/sickn33/chronochat Would love feedback, especially from people who use ChatGPT for long workflows. What would make this more useful for you? submitted by /u/Fickle_Guitar7417 [link] [comments]

I built a local MCP server that gives AI agents on-device Vision OCR no cloud, no API keys

Demo of how it works I got tired of sending documents and images to cloud APIs just to extract text, so I built VisionMCP a standalone MCP server that plugs directly into Apple's Vision Framework for on-device OCR (NOTE: It only works on macOS as it leverages the native on device Vision framework) What it does: PDF ingestion: renders pages to images via PDFKit, then runs RecognizeDocumentsRequest (the macOS 26 structured document OCR API). Extracts text, tables, lists, and paragraphs with confidence scores. Image ingestion: runs VNRecognizeTextRequest on PNG, JPEG, TIFF, BMP, GIF, HEIC, WebP whatever you throw at it (up to 250MB). Both paths return raw text, auto-chunked output (with configurable overlap), per-page confidence scores, and a SHA-256 file hash. Zero persistence, zero database purely read-only extraction. Why MCP? If you're using tools like opencode or any MCP-compatible AI client (like cLaUdEcOdE), you can just register the binary and your agent gets vision capabilities instantly. No wrapping scripts, no REST endpoints it talks over stdio. { "mcp": { "visionmcp": { "type": "local", "command": ["/usr/local/bin/visionmcp"], "enabled": true } } } Your agent can then call ingest_pdf or ingest_image with a file path and get structured text back. Tech: Swift 6.3, strict concurrency (Sendable everywhere) macOS 26 Tahoe + Xcode 26 Two independent parsers, no shared abstractions just direct routing Trade-offs: macOS 26 only (uses new Vision APIs) No Windows/Linux this is deeply tied to Apple's Vision framework Swift 6.3 strict concurrency means it's very safe but also very strict at compile time Repo: https://github.com/br3akzero/vision.mcp Also mirrored on Codeberg: https://codeberg.org/breakzero/vision.mcp Happy to answer questions or take feedback. PRs welcome. submitted by /u/DeChilli [link] [comments]

For those that follow the AI tech improvements, how long do you predict till AI will be capable of instantly Language Dubing Animes?

For those that follow the AI tech improvements, how long do you predict till AI will be capable of instantly Language Dubing Animes? Animes usually take a long time to dub into a different language. I been wondering if AI can help smooth that issue out by making a dub of the Anime within hours to near instantly. How long y'all think it will take till we get to that point with AI were it's capable of doing that? submitted by /u/Knighthonor [link] [comments]

Ron537/DPlex: Terminal multiplexer for AI-assisted development — manage Copilot CLI, Claude Code, and regular shells across projects in one window.

Hey everyone, Over the last few months, I’ve been heavily integrating terminal-based AI agents like claude-code and github-copilot-cli into my daily development workflow. They are incredibly powerful, but running multiple concurrent sessions across complex codebases quickly hits a major roadblock: workspace fragmentation. If you close your terminal, update your IDE, or reboot, your entire layout of splits, tabs, and active agent states vanishes. Trying to keep parallel feature branches, code reviews, and debugging sessions organized side-by-side gets messy fast. To solve this, I built DPlex—an open-source (MIT), local desktop workspace and terminal multiplexer optimized specifically for structured AI workflows. 💻 Landing Page: https://ron537.github.io/DPlex/ 📦 GitHub Repo: https://github.com/Ron537/DPlex What it does: * Absolute Layout & Tab Persistence: Quit the app, restart your machine, or let it crash—DPlex automatically serializes your exact environment to disk. Every single AI session tab, pane split, and active process restores perfectly back to where you left it. * Deep Git Worktree Integration: It features a project-aware sidebar designed around concurrent development. You can spin up side-by-side AI sessions in separate Git worktrees instantly, keeping your main branch clean while agents work on different features. * Unified Project Organization: Instead of loose terminal windows scattered across your desktop, DPlex groups your workspace by project. Switch between entirely different project environments with a single click. * Zero Telemetry & 100% Local: No cloud wrappers, no analytics, and zero external tracking. The source is completely grep-able and runs entirely on your local machine. Tech Stack & Architecture: It’s built to be modular. Adding support for a new AI agent provider is as simple as implementing a single pluggable TypeScript interface—no core forks required. It's available for macOS (Intel/Silicon), Windows, and Linux. I’d love to get your feedback on the layout workflow, feature requests, or any architectural thoughts. If you find it useful, please consider leaving a ⭐ on GitHub to help other developers discover it! submitted by /u/Ron537 [link] [comments]

Anthropic's new tool might just save you thousands in early design/mockup costs

If you are a founder, marketer, or product manager who struggles to translate ideas into polished visual prototypes without burning cash on an agency, you need to look at Claude Design. Anthropic Labs just launched it in research preview for paying Claude tiers (Pro/Team/Enterprise). It bridges the painful gap between having a product idea and having a high-fidelity visual asset you can actually show to clients or investors. Why this is a game-changer for early-stage builders: Instant Pitch Decks & One-Pagers: You can feed it raw data, a landing page draft, or a business model, and ask it to build a visual presentation deck or a polished corporate one-pager. "Vibe-Code" Your Prototypes: You can upload an image of a competitor's app or a napkin sketch, and tell Claude: "Build me a functional prototype that handles this workflow, but use our color scheme." Zero Setup Brand Rules: If you already have an existing web app or slide deck, you can upload them during onboarding. Claude automatically extracts your fonts, colors, and layouts so everything it builds stays visually consistent. Real Export Options: Instead of locking you into a proprietary ecosystem, it exports directly to Canva (for easy tweaking), PowerPoint (for pitching), or Raw HTML (so your engineers can instantly grab the layout structure). Early testers are already saying they can spin up a coherent, brand-compliant UI wireframe during a live meeting before people even leave the room. Has anyone gotten their hands on the research preview yet? How clean is the exported code/HTML structure for real web deployment? submitted by /u/Specialist_Engine522 [link] [comments]

No longer have access to extended pro or heavy thinking after UI update

submitted by /u/TheHolyToxicToast [link] [comments]

Glasses will fail

You are looking at the exact argument tech skeptics and infrastructure engineers are making right now. While the marketing for AI smart glasses promises a magical, seamless sci-fi world, the physical reality is that **AI glasses are heavily limited by the invisible infrastructure stack underneath them.** If AI glasses fail to become the next smartphone, it won't be because the hardware frames look bad; it will be because our modern networking and cloud structures aren't built to handle them yet. Here is exactly how infrastructure bottlenecks threaten to break the AI glasses dream: ### 1. The Tethering Trap & Cellular Bottlenecks To keep smart glasses lightweight and fashionable, manufacturers cannot pack them with heavy, heat-generating computer processors or massive batteries. Because of this, the glasses are mostly just "dumb" collectors of data—cameras and microphones. The heavy lifting has to happen in the cloud. This creates an immediate infrastructure dependency: * **The Upload Problem:** Standard cellular networks (even 5G) are optimized for *downloading* data (streaming video, browsing). AI glasses flip this dynamic—they require constant, high-bandwidth *uploading* of live video and audio streams so the cloud AI can process your surroundings. * **Network Congestion:** If you are in a crowded stadium, a packed subway station, or a busy downtown area, cellular bandwidth chokes. When your phone drops to one bar, your webpage loads slowly. When AI glasses lose bandwidth, they suffer **contextual blindness**—the AI simply stops responding, freezes, or lags out mid-conversation. ### 2. The Edge Compute & Latency Deficit For AI glasses to be useful, they have to operate in real time. If you look at a sign in a foreign country, you need the translation instantly, not 4 seconds later. ``` [ Glasses Capture Video ] ──(Cell Tower)──> [ Distant Data Center ] │ (Processing) [ Live Display Updates ] **The Takeaway:** The industry is fighting a classic hardware-versus-infrastructure battle. Companies like Meta and Google are successfully designing beautiful frames, but until 5G coverage expands, edge computing matures, and server architecture scales to handle millions of continuous video streams, AI glasses risk remaining a novelty gadget rather than a daily essential. > submitted by /u/Annual_Judge_7272 [link] [comments]

I built a zero-code visual client to test remote MCP servers instantly (Tested with Cloudflare’s free MCP).

Hey everyone, The Model Context Protocol (MCP) is amazing for standardizing how agents talk to data, but I got incredibly frustrated every time I wanted to quickly test a new remote MCP server. Writing custom client-side boilerplate or wrestling with CLI tools just to see if a tool actually exposes the right schema is a massive time sink. So, I built a native MCP client directly into the visual canvas of AgentSwarms. You can now test any remote MCP server entirely in the browser without writing a single line of code. Here is the workflow I just tested with Cloudflare: Cloudflare released a free MCP server for their documentation. Instead of building a local client to test it: I dropped their SSE URL into the new MCP Servers integration in AgentSwarms. The canvas immediately connected and extracted the available tools (e.g., cloudflare-docs-search). I wired that tool up to a basic agent and started asking complex infrastructure questions in natural language. The agent successfully used the MCP tool to pull live docs and synthesize an answer. Why this is useful for AI devs: If you are building your own MCP servers, you need a fast way to visually test if your endpoints are exposing tools correctly and if an LLM can actually route to them properly. This gives you an instant, visual debugging playground. It handles the SSE connection, tool extraction, and LLM routing automatically. It’s completely free to play with in the browser. I'd love for anyone building MCP servers right now to plug their endpoints in and see how it works. Link: https://agentswarms.fyi/mcp submitted by /u/Outside-Risk-8912 [link] [comments]

I built a zero-code visual client to test remote MCP servers instantly (Tested with Cloudflare’s free MCP).

Hey everyone, The Model Context Protocol (MCP) is amazing for standardizing how agents talk to data, but I got incredibly frustrated every time I wanted to quickly test a new remote MCP server. Writing custom client-side boilerplate or wrestling with CLI tools just to see if a tool actually exposes the right schema is a massive time sink. So, I built a native MCP client directly into the visual canvas of AgentSwarms. You can now test any remote MCP server entirely in the browser without writing a single line of code. Here is the workflow I just tested with Cloudflare: Cloudflare released a free MCP server for their documentation. Instead of building a local client to test it: I dropped their SSE URL into the new MCP Servers integration in AgentSwarms. The canvas immediately connected and extracted the available tools (e.g., cloudflare-docs-search). I wired that tool up to a basic agent and started asking complex infrastructure questions in natural language. The agent successfully used the MCP tool to pull live docs and synthesize an answer. Why this is useful for AI devs: If you are building your own MCP servers, you need a fast way to visually test if your endpoints are exposing tools correctly and if an LLM can actually route to them properly. This gives you an instant, visual debugging playground. It handles the SSE connection, tool extraction, and LLM routing automatically. It’s completely free to play with in the browser. I'd love for anyone building MCP servers right now to plug their endpoints in and see how it works. Link: https://agentswarms.fyi/mcp submitted by /u/Outside-Risk-8912 [link] [comments]

Tired of scrolling through long chatGPT threads so built an extension around it

I remember asking too many questions in a single thread, leading to the chat interface becoming laggy, slow, and frustrating to navigate. Whenever I needed to refer back to a specific prompt or code snippet, I had to manually scroll through a massive wall of text. Then I spent my time searching the web store for extensions to help with this, but only found some useless and some paid ones. So here is a free and open sourced extension that my friends and I now use daily to save time. It injects a clean navigation sidebar directly into the UI, allowing you to instantly bookmark and snap back to any message. A working demo video is attached to show the execution. Link to the codebase and extension is attached in the comments. I appreciate suggestions about this and should I also include other llms or any general suggestion you can offer . Thanks !! submitted by /u/leverageTheSpirit [link] [comments]

Vibe coded an algorithm that prints money

Been quietly working on this for the past year. tried to write it by hand at the start but decided to do 90/10 vibe code because it was too much work for a simple person. The idea is simple: Binance announcements move markets instantly and violently. The edge is being first (and the hardest part of the project). The system detects announcements the moment they hit, classifies them in sub microsecond, and simultaneously fires orders on multiple exchanges. It runs 24/7 on a dedicated AWS server in Tokyo,took a lot of painful lessons with exchange APls, WebSocket quirks, and latency optimization to get here but it's been worth it. Here is some examples of profits (| started with very small amount and added very slowly). Couldn't have done it without codex/claude code so yeah... This is obviously not a financial advice ! Just wanted to share something I have been building submitted by /u/Agreeable_Split1355 [link] [comments]