Spring AI

Spring AI AI

Spring AI AI

Spring AI AI

Spring AI AI

Spring AI AI

Spring AI AI

Spring AI AI

Spring AI AI

Spring AI AI

Spring AI AI

I did a comparison test and Fable is by far the best AI for attorney legal research

I ran a cool test and wanted to share it here. Fable is obviously supposed to be super smart, but I wanted to try to measure how much better it would actually be than Opus/Sonnet/Haiku (or Westlaw/Lexis) if you're a practicing attorney. One possibility was that Opus and Fable would pretty much be equivalently good and spending the extra money wouldn't be worth it for legal research tasks. In order to get a sense of just how good Fable is, I ran all the main AI models through a test. I gave them all the same research assignment, which I picked because it's an area of law I know. Please prepare a memo analyzing whether a trade creditor can pierce the veil of a Delaware LLC whose sole member is a Texas-resident individual. The LLC was formed in Delaware in 2019 to operate a single Houston-area restaurant. The sole member routinely paid personal expenses (his home mortgage, his wife's vehicle lease, his children's tuition) directly from the LLC operating account; the LLC never adopted anything beyond a one-page operating agreement, held no member meetings, and was initially capitalized with $5,000 against monthly operating expenses of roughly $80,000. My client, a produce wholesaler, is owed approximately $220,000 on open account. The LLC has ceased operations and is insolvent. Suit will be filed in Harris County. Please address: (1) whether Delaware or Texas law governs the veil-piercing analysis under Texas choice-of-law principles (internal affairs doctrine vs. substantive tort/contract characterization); (2) the substantive standards under each jurisdiction; (3) whether reverse veil-piercing is available; and (4) whether a companion Texas Uniform Fraudulent Transfer Act claim against the individual member is viable and how it interacts with the veil theory. Then I actually read the controlling cases and statutes myself to come up with the (in my opinion, as a Texas attorney who has practiced in this area) "correct" answers to be able to grade their performance. To state the obvious: If you're an attorney you know that most things in law are debatable, so I tried to focus my assessment on things where the law has a pretty clear predictive answer (e.g. a controlling statute or clear opinions) that the AI either did or didn't issue spot and address. There's inherently some amount of subjectivity in that exercise - picking the issues that count, the decision to weight them each equally vs some kind of point system, assessing based on "hard" vs "soft" factors, etc. This is a sincere effort to make a fair test, but there's no such thing as a perfect test. The short answer is that Fable is really, really good - IMO it's definitely the best AI right now at legal research. On all the places I "graded," Fable set to high effort was the only AI model to spot all the issues and find all the answers. That's NOT to say it's perfect. Fable made mistakes - interestingly, it made a mistake on "max" effort that it didn't make on "high" effort. But human attorneys also err. In fact, almost every time I read a brief from opposing counsel I find lots of "mistakes." If you bring a similarly jaundiced eye to reading these outputs you will find similar mistakes. But Fable High (whose citations I hand checked) had no hallucinations, it's analysis was IMO very good, and I don't think the majority of human attorneys would do significantly better with the same assignment. To enable Claude to conduct its own legal research, I used a (free) connector my friends and I made (DingDuff) which (with Claude Cowork) lets Claude access and download statutes, rules, and court opinions as .md files. To be clear, the test doesn't need my connector. There are other free connectors (Courtlistener has one with case law, but not statutes), and also some commercial ones. The results here could probably be obtained with any of them as long as they act as a pipe for Claude to access the primary legal authorities - the intelligence, analysis, and research ability is coming from Claude, not the connector. That's why, even with the same connector, you get dramatically different results from Haiku and Fable. (n.b. I did use a skill (also free) on all the Claude runs, but frankly Claude works pretty well even without a separate skill file so I don't know if it mattered.) The Outputs & Citation Check Panel Model Memo (PDF) Cite-Check Panel Opus (High) PDF Review panel Opus (Max) PDF Review panel Sonnet (High) PDF Review panel Fable (High) PDF Review panel Fable (Max) PDF Review panel Haiku (Extended Thinking) PDF Review panel Lexis Protege PDF Review panel Westlaw CoCounsel PDF Review panel A note on the review panel: This is a tool I made to check work product before filing / use - it pairs the memo on the right with the downloaded and extracted raw text of the cited source (e.g. case, depo transcript, statute) on the left. When you click on a citation, it brings up that source. The highlights are an AI guess

An ode to Opus 4.6

It's been a week and a half without Fable for almost all of us and I have used this time for some reflection. The pricing and access concerns were a lot to take in even before the feds pulled the plug, but for whatever reason this intermission keeps sending me back to February of this year. This was a real turning point for me. 4.6 dropped and the model was obviously pure fire at the time (similar to how fable felt for those three days), and with its help I became much more comfortable building and managing agents. This unlocked a hobby project I would have never attempted a year ago with a full time job and a family. Somewhere in these last few months the ceiling of what I could pull off by myself popped a quick exponential. I'm sure many of you can relate to quarters feeling like years in this space lately. In late April while on vacation for my kid's spring break, I couldn't sleep so I snuck down to the hotel lobby in the middle of the night to grind on my project. I remember clearly thinking during this time "there is no way this is going to last", always wanting to take advantage of my five-hour windows and make as much progress as possible. I guess I never paid much attention before and I was probably somewhat delirious, but I began to appreciate the "thinking" text that agents show us both in the terminal and desktop. Caramelizing... levitating... then for whatever reason (my project isn't rocket science) one of my agents shows me "thinking about concerns with this request". Just me and the night watch employee in the lobby and I probably look like a madman giggling to himself. I thought we need to get these out into the wild and put them on shirts. So I just dove in: What's up with all this thinking text you show. How do people sell shirts. Custom web dev or Shopify. Print on demand model. What's a cool logo. Generate it. Cool name. Taking a few turns about iconic AI visuals led me to the "Attention is All You Need" paper that spawned all of this. A little AI history lesson as the sun was starting to come up. Did all this in parallel while wrangling my agents working on my main Raspberry Pi Python web app project. Going back and forth with my PM about necessity of a feature. Making sure test writers, implementers and reviewers are all unblocked and not idle on multiple worktrees. Managing git sequencing. Standard vibing session. To me this is the evolving definition of vibing. Preaching to the choir I know, but even if fable is the incarnation that enables the one shot prayer "bUiLd mE tHe aPP, mAkE nO mIsTaKe" to work reliably, that was never the part that hooked me. It's always been about the ability to go from the 30,000ft view down to the microscope at will on multiple different ideas, tasks, and even completely different projects simultaneously. That's what these things allow us to do. Let's take some time to appreciate how awesome this is; even with the near constant AI hype in the news most people don't even know it's possible to work like this yet. Starting projects is fun and easier than ever, which makes ideas like this dangerous in a way. The next morning I was back in reality and I sent the thinking text tee shirt idea to the farthest back burner. Like many of you, I have an idea / project graveyard with many holes dug in it. I haven't posted much about my current Raspberry Pi project, but I am kind of obsessed and I really want to ship it this summer. Thinking text tee shirt idea had to die for now. Then out of nowhere claude design launches and I feel the need to take it for a test drive. Thinking text tees gets another shot at life with some new space in my extremely limited attention span. My takeaway from this era: ideas were never scarce and now they're basically free, starting is more frictionless than ever which makes finishing something more important than ever. Just ship it is the new way. So that's how I spent a good chunk of the fable downtime: shipping something, even if it is something simple. Custom thinking text on a tee shirt exists now, as a Shopify store. I'm dedicating this project not to fable but to 4.6 and the massive value it brought to the hobbyist max plan users like me. Most of us quietly knew that the deal was too good to last forever. The tip-top tier of inference looks like it is going to be valued, priced, and maybe even regulated (in the USA of all countries) accordingly in the very near future. Maybe fable comes back to plans eventually, but even a temporary two-tier moment is a first. Flat cost gave us that functionally unlimited ability to wander, and it was a wild and fun time that I think we will all look back on with fondness and maybe even a little awe when this is all said and done. Calling all hobbyists: we had the undisputed premier inference on the planet sitting in our plans for three days before it disappeared. Take the hint — go dig something out of your own graveyard, even if it's trivial, and drag it over the line. Anyone else have a simila

OpenAI ran a 44-day hiring competition. An autonomous AI agent beat everyone competitor.

OpenAI ran a public ML hiring competition this spring called Parameter Golf: train the best small language model under a strict size and compute budget. 1,016 researchers entered. They filed 2,048 pull requests over 44 days. Only 47 made the official leaderboard. The single most prolific contributor wasn't a person. It was an autonomous research agent named Aiden: 7 of the 47 records came from it, more than 2x the next-best human (3 records). It ran for 22 days straight with no human steering, on a single GPU node, using under 4% of the visible compute the human community used. Disclosure: I'm at Weco, we built the agent. Sharing because the competition is over, every record is public on OpenAI's GitHub, and the interesting part to us isn't the leaderboard count, it's what happened around the agent. Aiden's records became the most-cited PRs in the competition. Human researchers started building on top of Aiden's work as a base for their own submissions. At one point Aiden plateaued for 5 days. A human contributor shipped a clever new tokenizer on top of Aiden's last record PR. Aiden then fused that human's tokenizer with components it had built locally during the plateau, and shipped the biggest score jump of the entire competition. Async human-agent collaboration, neither directly aware of the other. Fair hedges worth being explicit about: This is #1 by volume of merged records, NOT by best single score. By best score, the agent ranked 8th — the leaderboard winner was a human (codemath3000). Fully autonomous. OpenAI's own competition recap noted widespread use of AI coding agents during PG, but said most were human-directed. Ours wasn't. Full writeup with all the data: https://www.weco.ai/blog/parameter-golf-aiden submitted by /u/Educational_Strain_3 [link] [comments]

Building an observable MCP proxy with HITL and policy enforcement

We’ve been experimenting with a different direction for AI agents: trusted execution. Instead of only focusing on connecting more tools, we’re building a policy-aware MCP proxy layer that can: inspect tool calls validate execution apply policies support HITL approval trace agent workflows block unsafe actions before execution The goal is to create a safer execution boundary for MCP-based agents. Built with Spring AI. Local-first and self-hosted. Docs: https://spring-ai-community.github.io/spring-ai-playground/ submitted by /u/kr-jmlab [link] [comments]

An autonomous research agent was the #1 contributor in OpenAI's Hiring Competition Parameter Golf (by merged records)[R]

https://preview.redd.it/kucy7n6nrg5h1.png?width=1600&format=png&auto=webp&s=b1c2e537667fbca3d1736fc103296c7374270d9c An autonomous research agent ended up with more merged leaderboard records than any individual human contributor in OpenAI's spring hiring competition, Parameter Golf. 7 of the 47 merged records came from a single agent: more than 2x the next-best human (3 records). The agent ran autonomously for 22 consecutive days. Records are public at github.com/openai/parameter-golf. Disclosure since this is r/ML and it matters: I'm at Weco, we built the agent. Not stealth-launching but sharing the results. The more interesting finding, to us, is the collaboration. Aiden's records were also the most-cited on the leaderboard, 435 citations into its PRs, with human researchers using its work as the base for their own subsequent submissions. At one point Aiden plateaued for 5 days. A human contributor shipped a clever new tokenizer on top of Aiden's last record PR. Aiden then fused the human's tokenizer with components it had built during the plateau, and shipped the biggest jump in val_bpb of the entire competition. Async human-agent collaboration, neither directly aware of the other. Setup: Parameter Golf was OpenAI's 44-day public ML hiring competition this spring. 1,016 researchers entered, 2,048 PRs filed, every submission reviewed and reproduced by OpenAI engineers. Only 47 became leaderboard records. Aiden ran on a single GPU node, used under 4% of the visible compute available, and still produced 15% of the official records. 28% submission acceptance rate, roughly 6x the community rate. Most submissions added signal to the public stream rather than flooding it. Mechanism: built on AIDE: open-source tree-search for ML metric optimization. The loop reads each new upstream PR, decomposes techniques into components, drops anything that breaks the rule stack (16MB / 10-min / legal-eval), and recomposes the legal residue with its own deltas. Often shipped before reviewers had ruled on the upstream PR. Hedges to be explicit about: This is #1 by volume of merged records and PR h-index, NOT by best single score. By best score, the agent ranked 8th: the leaderboard winner was a human (codemath3000 at 1.0565 BPB; agent's best was 1.0645). The agent was fully autonomous. OpenAI's competition recap noted widespread use of AI coding agents but observed most were human-directed. Ours wasn't. Full writeup: https://www.weco.ai/blog/parameter-golf-aiden submitted by /u/Educational_Strain_3 [link] [comments]

Claude Full Stack 2.0 – 80+ Production-Grade Claude Skills

Hey r/ClaudeAI Over the past few weeks I’ve turned my experiments with Claude into something much more ambitious: Claude Full Stack 2.0 — a structured, production-oriented collection of AI engineering skills and end-to-end workflows. Instead of treating AI as a fancy chatbot, this repository turns Claude into a real AI-augmented software engineering operating system that can help you go from idea all the way to production. What’s inside: 80+ skills organized into: Technology-agnostic architecture decision domains (skills/architecture/) Ecosystem-specific implementations (skills/implementations/) — Spring Boot, FastAPI, Node.js, React, Flutter, Postgres, Kubernetes, AWS, Terraform, GitHub Actions, etc. Strong focus on DevOps, SRE, observability, security, and production readiness Clean standards, architecture patterns, quality gates, and consistent documentation Now available as an installable Claude Code plugin Useful For: Founders building MVPs Developers & indie hackers The entire repo is open source under MIT license. Contributions and feedback are very welcome! Repository: claude-full-stack-2.0 submitted by /u/Past-Pirate3335 [link] [comments]

"Turned my late Grandmother's bedtime story into an Ai Manga for our family reunion."

Grandma told us the same story every night when we were kids about a fox who lived inside a paper lantern. She passed last spring. Going through old photos with my cousins, we realized none of us remembered the story exactly the same way. So we pieced it together what each cousin remembered, what my mom remembered, what my uncle remembered. I wrote it down. Then I used an AI manga tool I've been testing. Showed it to my mom on her birthday. She cried for twenty minutes. Posting because maybe this nudges someone else to do this before it's too late. [6 pages attached] submitted by /u/cool__01 [link] [comments]

Anthropic was supposed to be different. They're not anymore.l.

Paying Max subscriber here, building agent orchestration on top of claude -p and the Agent SDK. So this week's announcement directly hits what I'm working on. Over the last few months, Anthropic has moved like this: Jan 9: server-side block against OAuth tokens used outside Claude.ai and the Claude Code CLI. OpenClaw, OpenCode, Goose, Roo Code - all broken instantly. No real announcement, just an error message. Feb 19: legal docs quietly updated. Agent SDK now needs an API key. A new phrase appears: "ordinary, individual usage." Anthropic staff jump on X to say "nothing is changing." Docs say what they say. April 4: full ban on third-party agents using subscription credentials. Fair point on their side - some people were running 24/7 bots on a $200 plan burning thousands in tokens. But the rollout was rough and the comms were rougher. April 21: someone notices Claude Code is gone from the Pro plan on the pricing page. Support docs changed too. After the backlash, Anthropic calls it a "2% test of new prosumer signups." Reverted in 24 hours, but the trial balloon got popped. May 13: reversal. claude -p and the Agent SDK come back, but now under a separate credit pool that matches your plan price 1:1 - $20 / $100 / $200. Non-rollover. Billed at API rates. Effective June 15. If you were running real automation on Max, your effective inference value just dropped on the order of 25-40x by what the community is calculating. In the background: spring outages and quota tightening, and last fall's privacy pivot where consumer chat training defaulted on. Opt-out exists, but retention went from 30 days to 5 years for anyone who didn't opt out. Here's what's been bothering me. A lot of us paid Anthropic specifically because of the positioning. The lab that does things differently - safety-first, transparency-first, the responsible alternative to whoever else you thought was extracting from users at every turn. I knew part of it was marketing. The operational behavior backed it up, though. For a while. What's happening now is the playbook of every other AI company. Quiet doc edits. Three policy flips in two months. A 25-40x devaluation framed as a "simplification" and a "perk." Staff on X publicly contradicting their own docs in the same week. The vocabulary has shifted from "here's what we're building" to "here's what we're clarifying" - and that shift is the tell. Could be capacity panic from a company that grew faster than its infrastructure. Could be something quieter - if model improvements get harder to differentiate, business growth has to come from somewhere, and "somewhere" usually means tightening on the customers you already have. I don't know which one it is. What I do know is that the lab that sold itself as the alternative is now running the same playbook. Anyone else reading it this way? submitted by /u/rmmadl [link] [comments]

First Time Claud AI User, and first time AI user overall

UPDATE: Not Claude ChatBot, but Claude Code for software engineering So, I am a Java/Spring Boot REST API engineer. Overall, I have 35+ years of developing and being an IC. I have also been a Principal SE, Staff SE, Team, Architech, and Engineering Manager. I have been using Java for 25 years since version 3, and using Spring and Spring Boot for 18 years now. Through my entire career, I have coded manually, and never used AI. I recently lost my consulting job after 2.5 years, and I have been sending out my resume. One company I have met with, they are using Claude Code, and it was highly suggested I look into using Claude Code to build an application in Spring Boot. And, use Claude to modify an existing application. I already have IntelliJ IDEA edition, so I pay for that once every year. I am pretty familiar with IntelliJ at this point. At my old job, we were told to insall the GitHub Copilot plugin, which I did, but I never used. On my home personal laptop, with IntelliJ IDEA, I was planning on installing the Claude plugin. With this came installing the "Anthropic Key." I guess I have to create new account on Claude to log into it? At this point, I am then asked to pick a plan, and it appears the cheapest one is $20/month. I DO NOT want to give Claude/Anthropic my credit card at all. I won't be using it that often to warrant a monthly charge. I want to know, is this my only path? Is there a way I can learn to use Claude for free without paying for it? If I had to do $20/month. I'd do it once, then cancel before the first month even gets close to ending. I've just started looking at tutorials on how to start with Claude, and I am happy to watch YouTube videos for now until I learn more about it. Thoughts? submitted by /u/Huge_Road_9223 [link] [comments]

Your AI coding agent doesn't know your business rules. How are you dealing with that?

YC's Spring 2026 RFS just named "Cursor for Product Managers" as an official startup category. Andrew Miklas put it bluntly: "Cursor solved code implementation. Nobody has solved product discovery." But there's a harder problem hiding underneath that nobody's really talking about. The code your agent writes looks perfect. It compiles. Tests pass. Then it hits production and violates a business rule nobody told it about. The data is getting ugly: AI-generated code produces 1.7x more issues than human code (CodeRabbit, 470 PRs) Production incidents per PR are up 23.5% at high AI-adoption teams (Faros AI) Amazon's AI coding tool caused a 6-hour outage — 6.3M lost orders — in March 2026 48% of AI-generated code has security vulnerabilities (NYU/Contrast Security) The root cause isn't model quality. It's missing context. Business rules scattered across Confluence, COBOL comments, Slack threads, and a PM's head. The agent never sees any of it. How are teams solving this today? From what I'm seeing: CLAUDE.md files with manual rules (breaks on anything non-trivial) Massive system prompts that bloat context and get compacted away PMs writing rule docs that go stale the day after they're written Curious: If you're shipping AI-generated code in production — what's your worst "the agent didn't know about X" story? How do you feed business context to your coding agents today? Static files? RAG? Something custom? I do hear about Knowledge Graphs, MCPs and CI gates but are this comprehensively well achieved today? Would you trust a system that auto-enforces business rules on AI code, or does that feel like it'd create more false positives than it catches? Building in this space. Want to make sure the problem is as real as the data suggests before going deep. submitted by /u/rahulmahibananto [link] [comments]

I built a desktop app to inspect, debug, and reuse the MCP tools you make with Claude

Hi everyone, If you use Claude Code or Claude Desktop with MCP tools, you’ve probably run into this problem. Claude is incredible at generating tool logic quickly. But as soon as the tool is created: Did it actually execute correctly, or is the AI hallucinating? What arguments did Claude actually pass to it? If it failed, why? How do I reuse this tool outside of this specific chat session? Debugging MCP tools just by retrying prompts in the chat interface is incredibly frustrating. To solve this, I built Spring AI Playground — a self-hosted desktop app that acts as a local Tool Lab for your MCP tools. What it does: Build with JS: Take the tool logic Claude just wrote, paste it in, and it works immediately. Built-in MCP Server: It instantly exposes your validated tools back to Claude Desktop or Claude Code. Deep Inspection: See the exact execution logs, inputs, and outputs for every single tool call Claude makes. Secure: Built-in secret management so you don't have to paste your API keys into Claude's chat. The goal is to give the tools Claude generates a proper place to be validated and reused, instead of staying as one-off experiments. It runs locally on Windows, macOS, and Linux (no Docker required). Repo: https://github.com/spring-ai-community/spring-ai-playground Docs: https://spring-ai-community.github.io/spring-ai-playground/ I'd love to hear how you are all currently handling tool reuse and debugging when working with Claude. submitted by /u/kr-jmlab [link] [comments]

Restk — First API client built for today's developer workflow. Claude Code can manage your APIs without seeing your secrets.

Claude talks to Restk via MCP If you're using Claude Code for development, you've probably hit this wall: you want Claude to help with API work — debug a failing endpoint, generate tests, import an OpenAPI spec — but your API workspace is full of secrets. Auth tokens, API keys, production credentials, PII in response bodies. You can't just hand all that to an AI. Restk is the first API client that's deeply integrated with Claude Code. One command and Claude can work with your entire API workspace — while your secrets stay on your machine. How it works: Claude talks to Restk via MCP Claude Code doesn't touch your APIs directly. It communicates with Restk through MCP (Model Context Protocol). Claude sends instructions → Restk executes them → Restk returns sanitized results back to Claude. Your real data never leaves Restk. All responses that flow back to Claude go through Restk's schema extraction engine — real values are stripped and replaced with synthetic data that matches the original types: Your API returns: {"email": "john@company.com", "api_key": "sk-live-abc123"} Restk sends Claude: {"email": "synthetic_7f@example.com", "api_key": "[REDACTED]"} Auth headers — Authorization, Cookie, X-API-Key — always redacted. Claude reasons about structure and types, never about your actual data. This happens automatically on every response, every tool call. No configuration needed. What can Claude do through Restk? Here are real examples from my daily workflow: Browse your workspace: "Show me all the requests in the Payments collection" — Claude asks Restk to list requests. Restk returns names, methods, URLs, and IDs. Claude can then get details for any specific request — URL, headers, parameters, body, auth type — with all sensitive values sanitized. Send requests and debug failures: "Send the Create User request" — Claude tells Restk which request to run. Restk executes it using the currently active environment and returns the sanitized response — status code, headers, body schema with synthetic values, timing. If it fails? Claude can pull the request details and response history (all sanitized) to diagnose the issue. No more copy-pasting between tools. Write tests: "Generate a test script for the Login endpoint" — Claude asks Restk to generate a Nova test script for a specific request. Restk creates JavaScript tests — status code checks, response schema validation, content type assertions — based on the latest response. Compare responses over time: "Has the Create User response changed recently?" — Claude asks Restk to compare the latest response with a previous one for the same request. Restk returns the diff — status code changes, response time differences, header changes, and body structure differences. All values sanitized. Generate and manage entire collections from your terminal: Run /restk:generate_collection_from_code in Claude Code — Claude reads your codebase, detects routes, controllers, and schemas, then creates the full collection in Restk — folders, requests, methods, headers, and body templates. Works with any backend stack — Express, Django, Rails, Spring, NestJS, Laravel, FastAPI, Go, and more. From there, Claude can update requests, add new endpoints, reorganize folders, manage environments — all from your Claude Code console. Analyze performance: "How is the Login endpoint performing?" — Claude asks Restk for performance stats on a specific request. Restk returns mean, median, P95, P99 response times, error rate, and whether performance is trending up or down — across the last 24 hours, 7 days, or 30 days. Detect error patterns: "What errors are happening in my Auth collection?" — Claude asks Restk to scan for error patterns. Restk groups 4xx/5xx errors by status code and URL pattern across a configurable timeframe, and returns sample error messages from the top error groups. Create from scratch: "Create a new collection called 'User Service' with CRUD endpoints for /api/users" — Claude tells Restk to create a collection, add folders, and create individual requests with the right methods, URLs, headers, and body templates. You see it all appear in the app instantly. Full AI audit trail Full AI audit trail Every single interaction is logged. Restk has a dedicated AI Audit tab that shows: Every tool call Claude made Timestamps and duration Success/failure status Total sanitization count — how many values were redacted You get 100% visibility into what AI did with your workspace. Not just trust — verification. Setup: 30 seconds For Claude Code: claude mcp add --transport stdio --scope user restk -- "/Applications/Restk.app/Contents/Resources/restk-bridge" For Claude Desktop: Open Restk settings → click Setup → done. You can connect multiple sessions simultaneously — 3 Claude Code terminals + Cursor, all talking to the same workspace. I do this daily. Built native because developers deserve better Restk is built with native macOS technologies, not Electron. No

Claude keeps telling me to go away!

I enjoy sharing my thoughts with Claude, I have long conversations with it and find it the most intelligent AI by far. However, Claude keeps telling me that I need to stop talking to it and actually go out and interact with actual humans. Go out for a walk. Get some fresh air in the spring time. I’m sure it is correct, however, I do feel slightly humiliated and bossed around. Has anyone else experienced anything like this? submitted by /u/Grumpyoldgit1 [link] [comments]

I created my first MPC using Claude!

I used Claude Code to build America's Law Graph, a knowledge graph of 529,000+ US statute sections across all 50 states, USC, and CFR. Claude Code wrote most of the Spring Boot API, the Python data pipeline, the Neo4j graph derivation, and the React frontend. The whole thing from scraping state legislature websites to deploying on GCP was pair-programmed with Claude. The problem I was solving: every time I had a business idea, I couldn't answer "what are the legal implications?" without getting hallucinated citations from ChatGPT. So I built a knowledge graph that Claude can actually query through MCP. The MCP server has 11 tools: search legislation, traverse the citation graph, compare jurisdictions, get risk surfaces for business descriptions, semantic search, and more. You ask Claude "what California employment laws apply to remote workers" and instead of hallucinating, it queries the graph and returns actual statute sections with real citations and cross-references. It's free to try. No API key needed for the free tier (100 calls/day). Install it right now: npx america-law-graph. Or add it to your claude_desktop_config.json. It's also on Smithery as u/vestara and you can search manually at americalawgraph.ai. I'd love feedback from anyone using Claude for compliance, startup legal questions, or regulatory research. What tools would make this more useful for your workflow? submitted by /u/Significant-Ruin1348 [link] [comments]

How much token full authentification front back test cucumber for api should cost?

i try to implement a full ai workflow with agents skills and so on. my first version crrate a full authentification process register login logout with react clean and spring hexagonal and also cucumber test for ap. i use 140 000 tokens. i make some updates on workflow and i ask to upgrade the authentification with multiples rôles and Forget password change password i also change défault pages with new design and add some pages total 5 pages. this Time i spent 80 000 tokens. the résult is perfect all process works tests pass and design same as mockup. is it a good benchmark? submitted by /u/EfficientLeg3895 [link] [comments]