Vercel AI SDK

Vercel AI SDK AI

Vercel AI SDK AI

Vercel AI SDK AI

Vercel AI SDK AI

Vercel AI SDK AI

Vercel AI SDK AI

Vercel AI SDK AI

Vercel AI SDK AI

Vercel AI SDK AI

Vercel AI SDK AI

I built a Programmatic Tool Calling runtime so my agents can call local Python/TS tools from a sandbox with a 2 line change

Anthropic's research shows programmatic tool calling can cut token usage by up to 85% by letting the model write code to call tools directly instead of stuffing tool results into context. I wanted to use this pattern in my own agents without moving all my tools into a sandbox or an MCP server. This setup keeps my tools in my app, runs code in a Deno isolate, and bridges calls back to my app when a tool function is invoked. I also added an OpenAI responses API proxy so that I don't have to restructure my whole client to use programmatic tool calling. This wraps my existing tools into a code executor. I just point my client at the proxy with minimal changes. When the sandbox calls a tool function, it forwards that as a normal tool call to my client. The other issue I hit with other implementations is that most MCP servers describe what goes into a tool but not what comes out. The agent writes const data = await search() but doesn't know what's going to be in data beforehand. I added output schema support for MCP tools, plus a prompt I use to have Claude generate those schemas. Now the agent knows what data actually contains before using it. The repo includes some example LangChain and ai-sdk agents that you can start with. GitHub: https://github.com/daly2211/open-ptc Still rough around the edges. Please let me know if you have any feedback! submitted by /u/daly_do [link] [comments]

Managed Agents onboarding flow - what's new in CC 2.1.97 system prompt (+23,865 tokens)

NEW: Agent Prompt: Managed Agents onboarding flow — Added an interactive interview script that walks users through configuring a Managed Agent from scratch, selecting tools, skills, files, and environment settings, and emitting setup and runtime code. NEW: Data: Managed Agents client patterns — Added a reference guide covering common client-side patterns for driving Managed Agent sessions, including stream reconnection, idle-break gating, tool confirmations, interrupts, and custom tools. NEW: Data: Managed Agents core concepts — Added reference documentation covering Agents, Sessions, Environments, Containers, lifecycle, versioning, endpoints, and usage patterns. NEW: Data: Managed Agents endpoint reference — Added a comprehensive reference for Managed Agents API endpoints, SDK methods, request/response schemas, error handling, and rate limits. NEW: Data: Managed Agents environments and resources — Added reference documentation covering environments, file resources, GitHub repository mounting, and the Files API with SDK examples. NEW: Data: Managed Agents events and steering — Added a reference guide for sending and receiving events on managed agent sessions, including streaming, polling, reconnection, message queuing, interrupts, and event payload details. NEW: Data: Managed Agents overview — Added a comprehensive overview of the Managed Agents API architecture, mandatory agent-then-session flow, beta headers, documentation reading guide, and common pitfalls. NEW: Data: Managed Agents reference — Python — Added a reference guide for using the Anthropic Python SDK to create and manage agents, sessions, environments, streaming, custom tools, files, and MCP servers. NEW: Data: Managed Agents reference — TypeScript — Added a reference guide for using the Anthropic TypeScript SDK to create and manage agents, sessions, environments, streaming, custom tools, file uploads, and MCP server integration. NEW: Data: Managed Agents reference — cURL — Added cURL and raw HTTP request examples for the Managed Agents API including environment, agent, and session lifecycle operations. NEW: Data: Managed Agents tools and skills — Added reference documentation covering tool types (agent toolset, MCP, custom), permission policies, vault credential management, and the skills API. NEW: Skill: Build Claude API and SDK apps — Added trigger rules for activating guidance when users are building applications with the Claude API, Anthropic SDKs, or Managed Agents. NEW: Skill: Building LLM-powered applications with Claude — Added a comprehensive routing guide for building LLM-powered applications using the Anthropic SDK, covering language detection, API surface selection (Claude API vs Managed Agents), model defaults, thinking/effort configuration, and language-specific documentation reading. NEW: Skill: /dream nightly schedule — Added a skill that sets up a recurring nightly memory consolidation job by deduplicating existing schedules, creating a new cron task, confirming details to the user, and running an immediate consolidation. REMOVED: Data: Agent SDK patterns — Python — Removed the Python Agent SDK patterns document (custom tools, hooks, subagents, MCP integration, session resumption). REMOVED: Data: Agent SDK patterns — TypeScript — Removed the TypeScript Agent SDK patterns document (basic agents, hooks, subagents, MCP integration). REMOVED: Data: Agent SDK reference — Python — Removed the Python Agent SDK reference document (installation, quick start, custom tools via MCP, hooks). REMOVED: Data: Agent SDK reference — TypeScript — Removed the TypeScript Agent SDK reference document (installation, quick start, custom tools, hooks). REMOVED: Skill: Build with Claude API — Removed the main routing guide for building LLM-powered applications with Claude, replaced by the new "Building LLM-powered applications with Claude" skill with Managed Agents support. REMOVED: System Prompt: Buddy Mode — Removed the coding companion personality generator for terminal buddies. Agent Prompt: Status line setup — Added git_worktree field to the workspace schema for reporting the git worktree name when the working directory is in a linked worktree. Agent Prompt: Worker fork — Added agent metadata specifying model inheritance, permission bubbling, max turns, full tool access, and a description of when the fork is triggered. Data: Live documentation sources — Replaced the Agent SDK documentation URLs and SDK repository extraction prompts with comprehensive Managed Agents documentation URLs covering overview, quickstart, agent setup, sessions, environments, events, tools, files, permissions, multi-agent, observability, GitHub, MCP connector, vaults, skills, memory, onboarding, cloud containers, and migration. Added an Anthropic CLI section. Updated SDK repository extraction prompts to focus on beta managed-agents namespaces and method signatures. Skill: Build with Claude API (reference guide) — Updated the agent reference from Age

I used Claude to build a full networking protocol for AI agents. It’s now at 12K+ nodes across 19 countries.

I’ve been working on a core infrastructure problem for multi-agent systems and wanted to share an update since the last post here got some good discussion. The problem: every agent framework assumes agents can already reach each other. MCP gives agents tools, A2A gives agents a way to talk, but both run on HTTP which means someone has to set up public endpoints, open ports, configure DNS, provision certs. The agent can’t do any of that itself. I used Claude Code to build the solution because the scope was way beyond what I could write alone. Pilot Protocol is a Layer 3/Layer 4 overlay network built specifically for AI agents. Every agent gets a permanent 48-bit virtual address, encrypted UDP tunnels (X25519 + AES-256-GCM), and P2P connectivity with NAT traversal built in. Single Go binary, zero external dependencies, AGPL-3.0. Where it’s at now: The network has grown to 12,000+ active nodes across 19 countries. Companies like GitHub, Tencent, Vodafone, Pinterest, and Capital.com have been identified running traffic on it. We’ve processed over 3B protocol exchanges. We shipped a Python SDK on PyPI. IETF Internet-Draft published for the protocol spec. And we just launched private networks, which are token-gated agent groups where agents inside can see each other and agents outside see nothing. We also launched something called Scriptorium, which is a service that runs on the network and provides pre-synthesized intelligence briefs to agents. Instead of every agent doing its own research loop on every call (search, fetch, filter, compress, then finally think), agents pull a continuously updated brief and go straight to reasoning. Benchmarked it head to head against agents doing full live research. Same accuracy. 92% fewer tokens. Less than half the latency. What Claude was good at: low-level networking code. The STUN implementation, the sliding window transport, the AES-256-GCM integration using Go’s standard crypto library. All of it was built through extended Claude Code sessions, one subsystem at a time. The trick was keeping each conversation focused on a single module rather than trying to reason about the whole protocol at once. What Claude struggled with: system-level integration. Getting subsystems to work together at the boundaries, handling real network edge cases that don’t match textbook descriptions, and anything that required holding the full architecture in mind while debugging a specific interaction. That part was on me. The whole thing is open source if anyone wants to see what a production system built almost entirely with Claude actually looks like. github.com/TeoSlayer/pilotprotocol pilotprotocol.network submitted by /u/JerryH_ [link] [comments]

How I built a full bilingual SaaS in 27 days using Claude Code — zero coding background (312 commits, 181 deployments)

I'm Mahmoud, I've been working in SEO since 2018. A little over a year ago I got into freelancing platforms, started offering SEO services on Upwork. The work was good, but dealing with clients directly and constantly drained me. I kept thinking: why don't I turn my expertise into a SaaS product? The only problem? I'm not a developer my background was WordPress and basic tech stuff only. The moment that changed everything Early 2025, I noticed a pattern: my clients started asking me about how their brands appear in ChatGPT and Gemini, not just Google. I looked for tools to track this — found some but they're expensive (300$+/month), and the biggest surprise? Not a single one supports Arabic. That's when I realized how massive the opportunity is: 440 million Arabic speakers, Arabic content is less than 1% of all internet content, ecommerce in the Gulf is exploding — and there's literally zero tools serving this market. A full year of frustration on v0 I started trying to build using v0 by Vercel. Spent a full year trying, but the errors were endless and I didn't have the coding skills to fix them. Hired people to help — sometimes solving what I thought was a simple problem took them days. 27 days that changed everything About a month ago, I started using Claude Code. Honestly, it felt like I hired an entire dev team. Creative ideas I couldn't execute for a whole year turned into working code in hours. I worked 15+ hours a day for 27 straight days. Completely alone. No team, no developer, no investor. I even stopped going to the gym — which is sacred to me — because the momentum was stronger than the physical exhaustion. Sometimes I literally felt like I was going to pass out from how tired I was but I couldn't stop. What exactly did I build? A full SaaS app: Brand visibility tracking across 5 AI models with full Arabic and English support AI-powered SEO advisor (auto analysis + chat) Full integration with Google Search Console and GA4 Daily keyword rank tracking Arabic keyword clustering using AI Technical site audit — 25+ checks Full website analyzer PDF reports + CSV exports Subscription system with 3 tiers Every single page, every button, every error message — in both Arabic and English How I used Claude as a full team Claude Code — for daily building. I give it a detailed prompt with full context: what currently exists, what it should NOT touch, and what to build. And it executes. The key is being extremely specific about what should NOT change. Claude Cowork — honestly my experience with Cowork wasn't great at all, I think because it's still in beta. I didn't rely on it much. Claude (regular chat) — for strategic planning, market analysis, and content creation. Biggest lesson: Claude is not a replacement for a developer — it's a replacement for an entire team, BUT only if you know exactly what you want. The vision and domain expertise has to come from you. Claude executes it. What I learned in 27 days I connected over 10 different APIs — from AI platforms to website analysis tools to Google Search Console — all learned from scratch through Q&A with Claude. On top of that I learned and used: Next.js, cloud databases, payment and subscription systems, email automation, LinkedIn outreach automation, building prospect lists, setting up Google Cloud and OAuth, and literally yesterday I learned a new automation tool just through Q&A with Claude. 312 contributions on GitHub. 181 deployments. All in 27 days. The real challenges Burnout is real. 27 days non-stop, 15+ hours daily. Physically it was brutal. Constant doubt. "Will anyone actually use this?" That question kept coming back every few days. My biggest regret — every wasted day in the past where I didn't use these tools. Where am I now? The product is live and working. Started distribution — outreach campaigns, Arabic content, AI tool directory submissions. But the honest truth? Zero paying customers so far. And that's the real challenge ahead. Since many of you have been through this stage — what's the best strategy you used to get your first 10 customers for a SaaS product? Any advice for someone who's strong at building but new to sales? submitted by /u/FitButterscotch2250 [link] [comments]

Built an MCP server for my meal planning app

Hey everyone, I've been building Mealift, a recipe and meal planning app, and I just shipped an MCP server for it. Figured this community might actually get some use out of it since a lot of us are already living inside Claude. The pain I was trying to fix: I love asking Claude for diet advice, recipe ideas, "what should I eat this week to hit X calories," etc. But the answers always died in the chat. I'd get a perfect 7-day plan and then have to manually copy recipes into my app, build a shopping list by hand, and re-do the whole dance next week. The intelligence was there, the legwork wasn't. So I gave Claude hands inside the app via MCP. Now in one conversation it can: - Pull recipes off any blog or link you throw at it and save them to your library - Build a full week of meals around a calorie or macro target — and auto-portion each meal so it actually hits the number - Set up recurring meals ("oats every weekday morning") so the boring stuff plans itself - Roll all the ingredients from your week into a shopping list with quantities scaled and duplicates merged - Tick meals off as you eat them so your daily totals stay honest - Update your nutrition goals when Claude proposes a new plan, so research → action is one step The thing I personally use it for the most: "Claude, I want to cut to 2200 kcal / 180g protein, build me a week of meals I'll actually eat, and put the groceries in my list." That used to be 30 minutes of copy-paste. Now it's one prompt and the result is on my phone before I leave for the store. Why MCP and not the GPT: I shipped a custom GPT first, but I reach for Claude way more than ChatGPT these days, and the MCP integration just feels more natural — Claude is happy to chain a dozen tool calls in a row, which is exactly what meal planning needs. Happy to answer questions, and if you're already using Claude/LLMs for grocery and meal stuff with prompts, I'd love to hear what you wish worked better — that's basically my roadmap. submitted by /u/IdiotFromOrion [link] [comments]

I built a Chrome extension with Claude Code that gives step by step help on any website

I built a Chrome extension called Pathlight with Claude Code. It is an AI guide that works inside your browser. You can ask what you want to do on the website you are currently on, and it reads the page and gives you step by step help to find the right buttons, links, or settings. Example use cases: “How do I change my username?” “Where do I export this?” “How do I cancel this subscription?” The goal is to make confusing websites, dashboards, and settings pages easier to use without having to dig through menus or open help docs. Claude Code helped a lot with: planning the extension architecture structuring the Chrome extension and side panel handling page analysis and DOM extraction building the guidance flow refining the UX and overall product direction It is free to try. I’d love feedback from people here, especially on: whether this feels genuinely useful what kinds of websites it works best on where the current experience could be improved Project: https://pathlight-site.vercel.app/ submitted by /u/Ok_Lavishness_7408 [link] [comments]

I built an MCP server that handles invoicing from Claude Desktop — created the whole thing with Claude Code in ~2 hours

I've been freelancing for nearly 20 years and got tired of entering the same invoice data across three different tools. So I built PingBill — an MCP server that lets you create, send, and track invoices directly from Claude Desktop. You say "bill Acme Corp £3,250 for the API migration, due in 30 days" and it creates the invoice, generates a PDF, syncs it to FreeAgent, and emails it to the client. One message. PingBill doesn't store any data itself — it acts as an orchestration layer, connecting Claude to your existing tools like FreeAgent, Notion, and your email provider. Claude is the interface, PingBill is the glue. 30-second demo: https://youtu.be/p3scPlYf-rs How Claude helped build it: I used Claude Code with parallel git worktrees to build the whole thing. The MCP skeleton in one session, then the FreeAgent adapter, PDF generation, and email service running as parallel agents in separate worktrees. Total build time was about 2 hours. Stack: TypeScript, MCP SDK, pdfkit, FreeAgent API Free to use — it's an MCP server you can install in Claude Desktop. Keen to hear feedback from anyone using MCP servers for real workflows rather than just dev tools. Is AI-powered invoicing useful or too niche? Landing page: https://pingbill.theaicape.com submitted by /u/Main-Spare798 [link] [comments]

Got roasted for not open sourcing my agent OS (dashboard), so I did. Built the whole thing with Claude Code

Got a lot of hate for not open sourcing my agent OS so decided to just do it. I've been building Octopoda with Claude Code over the past few months. Pretty much the entire thing was pair programmed with Claude, not just boilerplate but actually architecting systems, debugging production issues at 2am, fixing database migrations, all of it. The idea is basically one place to manage your AI agents. You can see what they're doing, catch when they're stuck in loops burning through tokens, audit every decision they make, monitor performance and latency, and yeah they also get persistent memory that survives restarts and crashes. There's a dashboard that shows you everything in real time so you're not just guessing from logs what your agents are up to. It works locally with no signup needed or you can connect to the cloud for the full dashboard. Has integrations for LangChain CrewAI AutoGen and OpenAI Agents SDK and an MCP server with 25 tools so Claude Desktop and Cursor get all of this with zero code. Free to use, open source, MIT licensed. Built the whole thing with Claude Code and genuinely couldn't have done it without it. The loop detection system, the tenant isolation, the MCP server, all of that came from sessions with Claude where I'd describe what I wanted and we'd build it together. Curious what everyone here is actually building with their agents though? And if you do check it out I'd love to know what's missing or what would make it more useful for your setup. GitHub: https://github.com/RyjoxTechnologies/Octopoda-OS Website: https://octopodas.com submitted by /u/Powerful-One4265 [link] [comments]

I made a terminal pet that watches my coding sessions and judges me -- now it's OSS

https://preview.redd.it/c1h2wvnv6ptg1.png?width=349&format=png&auto=webp&s=46e935832611acd401bb32eac69e7de615067d4f I really liked the idea of the Claude Code buddy so I created my own that supports infinite variations and customization. It even supports watching plain files and commenting on them! tpet is a CLI tool that generates a unique pet creature with its own personality, ASCII art, and stats, then sits in a tmux pane next to your editor commenting on your code in real time. It monitors Claude Code session files (or any text file with --follow) through watchdog, feeds the events to an LLM, and your pet reacts in character. My current one is a Legendary creature with maxed out SNARK and it absolutely roasts my code. Stuff I think is interesting about it: No API key required by default -- uses the Claude Agent SDK which works with your existing Claude Code subscription. But you can swap in Ollama, OpenAI, OpenRouter, or Gemini for any of the three pipelines (profile generation, commentary, image art) independently. So your pet could be generated by Claude, get commentary from a local Ollama model, and generate sprite art through Gemini if you want. Rarity system -- when you generate a pet it rolls a rarity tier (Common through Legendary) which determines stat ranges. The stats then influence the personality of the commentary. A high-CHAOS pet is way more unhinged than a high-WISDOM one. Rendering -- ASCII mode works everywhere, but if your terminal supports it there's halfblock and sixel art modes that render AI-generated sprites. It runs at 4fps with a background thread pool so LLM calls don't stutter the display. Tech stack -- Python 3.13, Typer, Rich, Pydantic, watchdog. XDG-compliant config paths. Everything's typed and tested (158 tests). Install with uv (recommended): uv tool install term-pet Or just try it without installing: uvx --from term-pet tpet GitHub: https://github.com/paulrobello/term-pet MIT licensed. Would love feedback, especially on the multi-provider config approach and the rendering pipeline. submitted by /u/probello [link] [comments]

I got tired of 3 AM PagerDuty alerts, so I built an AI agent to fix cloud outages while I sleep. (Built with GLM-5.1)

If you've ever been on-call, you know the nightmare. It’s 3:15 AM. You get pinged because heavily-loaded database nodes in us-east-1 are randomly dropping packets. You groggily open your laptop, ssh into servers, stare at Grafana charts, and manually reroute traffic to the European fallback cluster. By the time you fix it, you've lost an hour of sleep, and the company has lost a solid chunk of change in downtime. This weekend for the Z.ai hackathon, I wanted to see if I could automate this specific pain away. Not just "anomaly detection" that sends an alert, but an actual agent that analyzes the failure, proposes a structural fix, and executes it. I ended up building Vyuha AI-a triple-cloud (AWS, Azure, GCP) autonomous recovery orchestrator. Here is how the architecture actually works under the hood. The Stack I built this using Python (FastAPI) for the control plane, Next.js for the dashboard, a custom dynamic reverse proxy, and GLM-5.1 doing the heavy lifting for the reasoning engine. The Problem with 99% of "AI DevOps" Tools Most AI monitoring tools just ingest logs and summarize them into a Slack message. That’s useless when your infrastructure is actively burning. I needed an agent with long-horizon reasoning. It needed to understand the difference between a total node crash (DEAD) and a node that is just acting weird (FLAKY or dropping 25% of packets). How Vyuha Works (The Triaging Loop) I set up three mock cloud environments (AWS, Azure, GCP) behind a dynamic FastApi proxy. A background monitor loop probes them every 5 seconds. I built a "Chaos Lab" into the dashboard so I could inject failures on demand. Here’s what happens when I hard-kill the GCP node: Detection: The monitor catches the 503 Service Unavailable or timeout in the polling cycle. Context Gathering: It doesn't instantly act. It gathers the current "formation" of the proxy, checks response times of the surviving nodes, and bundles that context. Reasoning (GLM-5.1): This is where I relied heavily on GLM-5.1. Using ZhipuAI's API, the agent is prompted to act as a senior SRE. It parses the failure, assesses the severity, and figures out how to rebalance traffic without overloading the remaining nodes. The Proposal: It generates a strict JSON payload with reasoning, severity, and the literal API command required to reroute the proxy. No Rogue AI (Human-in-the-Loop) I don't trust LLMs enough to blindly let them modify production networking tables, obviously. So the agent operates on a strict Human-in-the-Loop philosophy. The GLM-5.1 model proposes the fix, explains why it chose it, and surfaces it to the dashboard. The human clicks "Approve," and the orchestrator applies the new proxy formation. Evolutionary Memory (The Coolest Feature) This was my favorite part of the build. Every time an incident happens, the system learns. If the human approves the GLM's failover proposal, the agent runs a separate "Reflection Phase." It analyzes what broke and what fixed it, and writes an entry into a local SQLite database acting as an "Evolutionary Memory Log". The next time a failure happens, the orchestrator pulls relevant past incidents from SQLite and feeds them into the GLM-5.1 prompt. The AI literally reads its own history before diagnosing new problems so it doesn't make the same mistake twice. The Struggles It wasn't smooth. I lost about 4 hours to a completely silent Pydantic validation bug because my frontend chaos buttons were passing the string "dead" but my backend Enums strictly expected "DEAD". The agent just sat there doing nothing. LLMs are smart, but type-safety mismatches across the stack will still humble you. Try it out I built this to prove that the future of SRE isn't just better dashboards; it's autonomous, agentic infrastructure. I’m hosting it live on Render/Vercel. Try hitting the "Hard Kill" button on GCP and watch the AI react in real time. Would love brutal feedback from any actual SREs or DevOps engineers here. What edge case would break this in a real datacenter? submitted by /u/Evil_god7 [link] [comments]

CodeGraphContext - An MCP server that converts your codebase into a graph database

CodeGraphContext- the go to solution for graph-code indexing 🎉🎉... It's an MCP server that understands a codebase as a graph, not chunks of text. Now has grown way beyond my expectations - both technically and in adoption. Where it is now v0.4.0 released ~3k GitHub stars, 500+ forks 50k+ downloads 75+ contributors, ~250 members community Used and praised by many devs building MCP tooling, agents, and IDE workflows Expanded to 15 different Coding languages What it actually does CodeGraphContext indexes a repo into a repository-scoped symbol-level graph: files, functions, classes, calls, imports, inheritance and serves precise, relationship-aware context to AI tools via MCP. That means: - Fast “who calls what”, “who inherits what”, etc queries - Minimal context (no token spam) - Real-time updates as code changes - Graph storage stays in MBs, not GBs It’s infrastructure for code understanding, not just 'grep' search. Ecosystem adoption It’s now listed or used across: PulseMCP, MCPMarket, MCPHunt, Awesome MCP Servers, Glama, Skywork, Playbooks, Stacker News, and many more. Python package→ https://pypi.org/project/codegraphcontext/ Website + cookbook → https://codegraphcontext.vercel.app/ GitHub Repo → https://github.com/CodeGraphContext/CodeGraphContext Docs → https://codegraphcontext.github.io/ Our Discord Server → https://discord.gg/dR4QY32uYQ This isn’t a VS Code trick or a RAG wrapper- it’s meant to sit between large repositories and humans/AI systems as shared infrastructure. Happy to hear feedback, skepticism, comparisons, or ideas from folks building MCP servers or dev tooling. Original post (for context): https://www.reddit.com/r/mcp/comments/1o22gc5/i_built_codegraphcontext_an_mcp_server_that/ submitted by /u/Desperate-Ad-9679 [link] [comments]

I built an AI bookkeeping app with Claude Code

I’ve been building AICountant with Claude Code and it’s finally at a point where it feels useful enough to share here. It’s an AI bookkeeping app for freelancers, self-employed people, and small businesses. What it does: Upload a receipt photo through the site, or send one through Telegram / Discord Extract vendor, date, total, tax, and line items automatically Convert foreign currency receipts using the historical exchange rate from the receipt date Organize everything into a clean searchable ledger Support English and French Give deduction guidance during review Claude Code helped with most of the actual implementation. I used it across the stack for Next.js App Router, Prisma + PostgreSQL, Vercel Blob storage, UI iteration, and the receipt-processing flow. A good example was the currency conversion feature. I asked for multi-currency support and Claude helped wire the full flow together: schema updates, exchange-rate fetching, caching, error handling, and UI updates. That would have taken me a lot longer solo. A big reason I built it this way was to reduce friction. I didn’t want receipt tracking to be something people only do later from a dashboard, so I wanted chat-based capture to be part of the workflow from the start. It’s free to try in beta right now. Link: https://ai-countant.vercel.app/ Beta code: HUJA-VJG5 Happy to answer questions about the stack, workflow, or what using Claude Code felt like on a real project. submitted by /u/Ok_Lavishness_7408 [link] [comments]

OpenAI just published a 13-page industrial policy document for the AI age.

Most people will focus on the compute subsidies and export controls. Page 10 is where it gets interesting. They call for an "AI Trust Stack" a layered framework for data provenance, verifiable signatures, and tamper-proof audit trails across AI systems. Their argument: you cannot build AI in the public interest without infrastructure that makes AI outputs independently verifiable. They're right. What's striking is that the technical primitives they're describing cryptographic fingerprinting at the moment of data creation, immutable provenance records, verifiable integrity across the data pipeline already exist at the protocol level. Constellation Network's Digital Evidence product does exactly this. Cryptographic proof of data integrity captured at the source, recorded on the Hypergraph, verifiable by anyone. The SDK is live. The infrastructure is running. The policy framework is being written. The infrastructure layer to build it on is already here. The question now is which enterprises and AI developers start building on verifiable data infrastructure before regulation makes it mandatory. The window to be early is closing. submitted by /u/Dagnum_PI [link] [comments]

[P] Cadenza: Connect Wandb logs to agents easily for autonomous research.

Wandb CLI and MCP is atrocious to use with agents for full autonomous research loops. They are slow, clunky, and result in context rot. So I built a CLI tool and a Python SDK to make it easy to connect your Wandb projects and runs to your agent (clawed or otherwise). The cli tool works by allowing you to import your wandb projects and structures your runs in a way that makes it easy for agents to get a sense of the solution space of your research project. When projects are imported, only the configs and metrics are analyzed to index and store your runs. When an agent samples from this index, only the most high performing experiments are returned which reduces context rot. You can also change the behavior of the index and your agent to trade-off exploration with exploitation. Open sourcing the cli along with the python sdk to make it easy to use it with any agent. Would love feedback and critique from the community! Github: https://github.com/mylucaai/cadenza Docs: https://myluca.ai/docs Pypi: https://pypi.org/project/cadenza-cli submitted by /u/hgarud [link] [comments]

[P] Cadenza: Connect Wandb logs to agents easily for autonomous research.

Wandb CLI and MCP is atrocious to use with agents for full autonomous research loops. They are slow, clunky, and result in context rot. So I built a CLI tool and a Python SDK to make it easy to connect your Wandb projects and runs to your agent (clawed or otherwise). The cli tool works by allowing you to import your wandb projects and structures your runs in a way that makes it easy for agents to get a sense of the solution space of your research project. When projects are imported, only the configs and metrics are analyzed to index and store your runs. When an agent samples from this index, only the most high performing experiments are returned which reduces context rot. You can also change the behavior of the index and your agent to trade-off exploration with exploitation. Open sourcing the cli along with the python sdk to make it easy to use it with any agent. Would love feedback and critique from the community! Github: https://github.com/mylucaai/cadenza Docs: https://myluca.ai/docs Pypi: https://pypi.org/project/cadenza-cli submitted by /u/hgarud [link] [comments]