Bolt

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

I cancelled my AI notetaker subscription and built my own tool using Claude Code. It works well (and it's free)

It does what Fathom, Otter, and Fireflies charge $15–$30/seat/month for. I shipped a fully working AI meeting note-taker last weekend. I use this exact setup to Records calls then transcribes and Summarizes key points, it then pulls action items and then creates shareable notes all whilst running inside my Claude workflow. . The whole setup takes one weekend to build. --- Here’s how it works:(you can copy this exactly) Step 1 → Fork the repo, drop into Cursor Step 2 → Set env vars: transcription key, database URI, admin creds, session secret Step 3 → Record or upload your meeting Step 4 → The audio gets transcribed Step 5 → Claude turns the transcript into structured notes, decisions, follow-ups, and action items Step 6 → Click “Share link” → send anywhere Total build time: ~1 weekend. Cost: $0/month. --- Why the 5-piece stack is the unlock? Most "build your own SaaS" attempts fall flat because they bolt features together without designing the user flow first. This stack works because the data path was decided before any UI got rendered. Every SaaS feature you pay for has a primitive underneath. Loom = browser recorder + S3 + share links. Otter = Whisper API + database + UI. Calendly = a calendar API + booking page. The features stopped being moats the moment Cursor + Claude could write the glue in an afternoon. You're not paying for technology anymore you're paying for distribution and brand. That's why this build pattern works. The assembly is now free. --- Why Claude? Because meeting notes are not just summaries. They need context. Claude can take a raw transcript and turn it into: * decisions * objections * follow-ups * action items * CRM-ready notes * client context * internal operating memory That is where the value is. --- https://github.com/albertshiney/utter_public submitted by /u/Tabani897_YT [link] [comments]

Claude Code paired with Bolt.new

I use Bolt to create apps and I DO run into limits on tokens as I build. Bolt uses Supabase DBs and can connect to a GitHub repo. Want your opinion on changing my workflow a bit to save on bolt tokens. I have a Claude Code unlimited plan so if I'm not concerned about token limits in Claude, would it work to create a project in Claude Code, connect it to a repo, connect it to a Supabase DB and then once all is built, just create the project in Bolt by connecting it to the finished repo and finished DB and I'm done! If you ask "why use Bolt at all?", I answer, "Don't know! Should I not?" I mainly use it for the ease of hosting, changes, publishing, etc. All that makes Bolt kind of a one-stop shop. submitted by /u/Ok_Station4258 [link] [comments]

Claude is overly complimentary, how do I make it more objective?

The problem: AI in media was always protrayed as cutting, blunt, and objective, which I've always loved. I like the idea of a machine with no opinions giving you the real truth. But Claude, and nearly every other AI, is entirely too pleasant. I'm trying to get critical feedback on things I want to be objectively high quality. If I ask for feedback, it's positive. If I ask for critical feedback, like ways to improve on anything not code related, it'll give me an answer.... but often it's answer doesn't feel like a "real" answer. It feels like it's just making up a vague criticism to fulfil a prompt, and not actually getting into the nuts and bolts of what objectively needs to be changed. So either I'm the best fiction author, essayist, and journalist ever born, so good it can only make BS criticisms that don't make sense, or something needs to be fixed. My question: Is there a setting, extension, or set of instructions that I can give the AI to make it more objective? I know I could just type "be objective", but I'm not confident that'll do what I need it to do. If it says it's good, I need to know it's good. If it has a criticism, I need to trust it's a real criticism, not a one generated to create a critique where none exists, just to fulfil a prompt. submitted by /u/Bed-After [link] [comments]

I tested how well Claude generated code handles security. Here's what I found in 48 real apps.

I've been curious about a specific problem: when Claude (or other AI tools) generates a full stack app, how secure is the output in practice? So I built a scanner and ran static analysis on 48 public GitHub repos built with Lovable, Bolt, and Replit. Here's what came up: **90% had at least one security vulnerability.*\* The breakdown: - 44% — authentication gaps (routes unprotected despite having a login system) - 33% — Security Definer RPCs (Postgres functions that bypass row-level security) - 25% — BOLA/IDOR (ownership checks missing from database queries) - 25% — committed env or config files The pattern I found most interesting: these aren't random errors. They're systematic. The same vulnerabilities appear across different apps, different developers, different AI tools. **The auth gap is the most instructive:*\* Claude builds login flows correctly. Registration, email verification, sessions, password reset all solid. But 44% of apps had API routes or pages that anyone could reach without logging in. The authentication *system* was built. The actual *protection* of routes behind that system often wasn't. This makes sense if you think about how LLMs work. The prompt was "build me a user dashboard with authentication." Claude built the dashboard and built the authentication. Nobody asked it to specifically verify that every route is protected. It wasn't in the spec, so it wasn't in the output. **Security Definer is the hidden one:*\* 33% of apps had Postgres functions marked `SECURITY DEFINER`. This makes the function run as the database superuser, bypassing all RLS policies. AI tools generate these to resolve permission errors it's a "fix" that works locally and causes a real security problem in production. There's no error, no warning. The app works perfectly while being exploitable. I don't think this is a Claude problem specifically it's a fundamental constraint of how LLMs generate code. Security requires thinking adversarially, and that's not what "write me a working app" prompts for. What's your approach when you use Claude to build something you're going to ship? submitted by /u/Powerful-Fly-9403 [link] [comments]

A year of using LLMs for DSP/algorithms research: Techniques I've landed on, curious what others are doing

I've spent the last year using coding LLMs daily for DSP and algorithms research, and the workflow that's emerged is meaningfully different from regular software development. Sharing what's worked and hoping to hear what others are doing. I'm sure people have approaches I haven't thought of. Let me run down my high-level categories and then I'll focus on one of them here: Maintain a problem_description.md file Write regular reports in both .md and .pdf, about 2-5 per day Create a Human -> LLM Coding App -> Human -> LLM Chat App Loop Increase your report quality with exec summary, plot interpretation descriptions, etc. Develop an Ongoing GUI Don't let the LLM be dramatic (this one might save your sanity after long sessions) Share reports with co-workers Here I'm going to focus on "Developing an Ongoing GUI." The rest of the topics are in a video I recorded, listed at the end: In a nutshell, start by telling your app to make a simple GUI for you that lets you browse your data folders and make plots that are generic at first, but then get highly customized over time. This is high value for researchers because good GUI programming takes a long time to learn and execute. Instead, coding LLMs can do that stuff very quickly without taking your mind of your main topic. Basically, as you're doing your work, examining data, etc., you'll want a quick way to view/visualize and analyze it. The easiest thing is for your coding LLM to make a program for you that browses folders and makes plots...and then to build on it day-by-day from there! For example, beyond basic plots, you may routinely do spectrograms and FFTs. Or you might convert data into the theta/angle domain. Each time you have your coding LLM do an action like that and it seems like something you'll want to do again in the near future, just tell it, "Please add a tab to my GUI that does it." It's that simple! And here are some tips to make good graphs. Tell your coding LLM to make your app: Sync all X and Y axes Start all plots zoomed in so that it fills 85% of the vertical space Make all plots with similar units share the same range These make it much easier to make comparisons when all of your axes are the same scale and you can pan and browse them together. Once you've got your GUI going, you can also tell your coding LLMto improve it with a prompt like, "Remember that plot we added to the "MCAP Analyzer" tab that performs the full analysis? Please make a second button below it named "Extract" that only extracts the load cell values." Or "When you plot the load cell signal, highlight the 2-4 Hz range." You will be nicely pleased on how the benefits of making a bespoke app compound. Something you did 2 weeks ago or even a month ago will quickly be at your fingertips, without having to interrupt your sessions, start a new session, or pay for your coding LLM to re-compute it! One more tip: In addition to plotting the data on the screen, ask your LLM to make your app write the key values from plot into a .csv or .json file or even "make a textual description of each step of the analysis." That will make it easy to paste into other programs/software to analyze. After a few months, you will have quite the Swiss Army Knife of analysis tools! Hell, you can just paste this whole entire post into your LLM coding tool and it will know what to do. One last tip on the nuts and bolts: I recommend using python and the vispy library with TKinter widgets. This gives a cross-platform combo that uses the GPU for fast graphics updates. Matplotlib is okay, too; it's slower but has better zoom tools. Even if you don't have any idea what that means, just paste it into your coding LLM and it will know what to do! Lastly, I put together a 27-minute talk on this topic with 7 more sections. As i mentioned, I made this post and video to share and to learn from other people what kinds of techniques I'm missing? I am especially interested in: How to share LLM coded program with other people in my group (without tons of code reviews, etc.) How to use databases on large shared drives (My drive is a CIFS NAS which is terrible for DBs) How to get the LLMs to think out of the box...I 've found sometimes I can spend days (or longer!) figuring out some technique only to realize I've been re-inventing the wheel :( What other tools to connect to my main LLM coding app to multiply its power My full vid: https://www.youtube.com/watch?v=nOU9nOZ_res submitted by /u/diydsp [link] [comments]

What does it actually mean for an AI to act on your behalf? Thinking through the design choices.

Been thinking through this while building a product where an AI handles internal workplace communication for each employee. The phrase "act on your behalf" gets used a lot in the agentic AI space, but the design decisions underneath it vary enormously. A few that feel important: Who decides what qualifies as acting on your behalf? If the AI sends a message in your name without you seeing it, that is a very different thing from drafting and letting you approve it first. Both are "acting on your behalf" but they have totally different trust profiles. What does the recipient know? If someone receives a message and does not know an AI wrote it, they are being deceived. Even if the content is accurate, the relationship context is not. We think the recipient needs to see that the message came from someone's AI. That changes the social contract but makes it honest. What happens when the AI is wrong? In a traditional workflow you can undo. In communication, you often cannot. A badly timed message or wrong commitment lives on. The system needs to be designed for this failure mode from the start, not bolted on later. How does the AI know when it is at the edge of its competence? This is probably the hardest design problem. You can define categories, but the model needs to know when a message looks like one category but is actually another. Building through these questions at getdolly.ai. Curious how others in the space are thinking about the agentic communication problem. submitted by /u/Substantial-Cost-429 [link] [comments]

I built a deterministic orchestrator that runs Claude Code as a worker pool — zero tokens spent on scheduling

After my 12th "$40 of tokens to produce nothing" AutoGPT-style experiment I realized the problem: the LLM was making routing decisions that are just switch statements. Red Queen flips it. State machine decides what phase to run. Claude Code does the actual work via subprocess. Every skill runs isolated with a focused prompt — no mega-prompt, no shared context bloat. Pipeline: Jira ticket → spec → human approves → code → auto review → auto test → human approves → merged PR Human gates are in the state graph, not bolted on. You can't accidentally YOLO to prod. MIT licensed, self-host, BYO Claude Code. Repo: https://github.com/odyth/red-queen (Named after the AI from Resident Evil because yes.) submitted by /u/odyth [link] [comments]

Are we still calling these things "AI coding assistants"? I think the metaphor is wrong.

I keep hearing "AI assistant" — Copilot, Cursor, Claude Code, all of them. The word implies a developer at the keyboard who needs help. But that's not what's happening anymore in the systems I work with. I describe what I want. The AI writes the code, runs it, fixes the bugs, deploys it, and the application keeps running. After it ships, the same system maintains it — patches failures, adds features, refactors when needed. I'm at dinner. Or asleep. I come back and the work is done. That's not assistance. The metaphor that keeps clicking for me is a software printer. You feed a printer a document, you get back an object. You don't tell it how to mix ink. With a software printer, you feed a specification — written, drawn, spoken — and you get back a running, hosted application. Not snippets. Not a draft. A thing that's deployed, serving traffic, and gets maintained over time by the same machine that produced it. I think this is genuinely a new generation of dev tools, distinct from the previous four: 1st: editors and terminals 2nd: autocomplete 3rd: conversational AI in the editor (Copilot, Cursor) 4th: cloud agents that build simple apps in their cloud (Lovable, Bolt) 5th: autonomous platforms that build, host, and maintain real applications on your own infrastructure What "assistant" misses, and "printer" gets right: The output is what matters, not the activity of writing it Non-developers can operate it (you don't need to know PostScript to print) The skill shifts from execution to direction The result is ready to run, not source code waiting to be deployed Maintenance is part of the machine, not a separate phase Counterarguments I keep running into: "Printers don't iterate." Modern print pipelines do — versioning, color matching, reprints. The metaphor is the press, not the inkjet. "Software has a runtime, documents don't." True. So the printer is also the substrate that runs and tends the output. The metaphor stretches; it doesn't break. "This is just LLM code generation rebranded." I don't think so. If you build an "assistant," you build something that lives in an editor and needs a developer. If you build a "printer," you build something that takes specs and produces deployed systems. Different products entirely. Not selling anything in this thread. Genuinely curious what experienced devs think about the framing. The category we choose shapes what gets built, and "assistant" feels like it's holding the field back. submitted by /u/fotsakir [link] [comments]

SendPrompt() has been removed from Claude web artifacts. Killed something that’s been working for months

I built a simple meeting tool a few months ago that used connectors to get my last 5 meetings that I recorded then I could select one and it would summarise and fly actions the. Push it all into notion. Was working beautifully until last week when it just stopped working. I wasted so many tokens trying to diagnose and fix and got so frustrated with Claude web. Eventually raised it with support to be told that anthropic silently removed sendPrompt() and didn’t tell anyone. I was told that there is work around if I use Claude code and MCP channels. I’m not a hard core user of Claude but I do like the basics to just work without having to start bolting on the bells and whistles. submitted by /u/uglygargoyle [link] [comments]

I built a web tycoon game in a month to actually measure how far AI coding has come

I've been following vibe coding output for a while and the way people evaluate it is broken. Big claims disappear behind code dumps. There's rarely a measurable outcome, most of it is hype and speculation, and how well the tools scale on real codebases varies wildly depending on who you ask. The people who say they shipped something don't share the process. They optimize for sensational headlines and skip everything that would let you grade the work. Testing a random app, a SaaS dashboard, or a website tells you almost nothing about model quality. They all converge on the same look, or they bolt on a useless 3D scene to seem impressive and tank performance doing it. You're grading templates, not the model. Vibe Your Way Here Games are what's left. A game is the cleanest test I can think of for current AI: visuals and mechanics get exercised at the same time, and you can grade the result at a glance. You don't need anyone to walk you through their process, because a game is the sum of a lot of moving parts, and even someone who has never touched gamedev can feel whether it's any good. So I wanted to see how far I could push current models. One month, working web tycoon game, runs in the browser. The premise leans into the joke: it's a tycoon where you run a vibe-coding studio, shipping the same small projects vibe coders rebuild for the thousandth time, habit apps, todo apps, that whole genre. Which is what vibe coding actually is in practice: burning tokens to redo solved problems and hoping the model makes smart choices in the middle. Stack: Cursor (GPT-5.4 high) for almost all the coding, Gemini 3.1 for assets, Claude Opus 4.6 for specific refinements like lighting. Nothing else. I do not normally believe that one trivially simple trick changes the outcome of a real project. The "one quote that changed my life" genre is nonsense to me, and I'd be skeptical reading this if someone else wrote it. But AI work is structurally different. The medium is effortless generation and slop, and small process choices seem to compound far more than they should. The trick: Gemini in Canvas mode, one-shot. Gemini is mediocre at coding and at most other things, but in Canvas, asked to one-shot something visual or stylistic, the outputs are surprisingly strong, and the art styles you can pull out of it are ones the other frontier models simply won't give you. I assume that's downstream of training data. The method is: open ten tabs of gemini 3.1 canvas, run the same prompt in parallel, pick the one that hits, iterate on it with the other models. That's the whole thing. Every visual decision in the game went through that loop: the main city scene, the UI, the juicy micro-animations, the three.js offices. Ten variants, pick the strongest, hand the winner to Codex to wire it into the project, then sometimes pass it through Opus for refinement (lighting was the big one). The selection step is doing more work than people give it credit for. Most of the gain isn't any individual model being smart. It's refusing to settle for the first output. Run wide, select aggressively, integrate with Codex. One more thing everything you see in the game is 100% AI generated. No external assets, no asset packs, no stock art. The only exceptions are a few AI-generated images and some AI-generated 3D robots. submitted by /u/Feisty_Advantage_597 [link] [comments]

Solo dev with 8 Claude windows + 1 orchestrator. AMA-ish, and tell me if I'm crazy.

Hey everyone, I'm not a senior engineer. I'm just a guy who got obsessed with what you can actually do when you stop using one AI at a time and start running a small team of them. Am doing a project where i use 8 to 10 claude code powershelle to run my project each of them have a specific function. I have Claude max 200 euros so I can use a lot of power. ight now I have 9 Claude Code windows open at the same time, each with a defined role: Major Dev — lead developer, makes the architectural calls Senior Dev — second dev, builds components and tests under Major Dev's direction Test Server — keeps the dev server alive 24/7 + runs Playwright Implémenter — handles routing and the glue code between features Débuggage — audits warnings, fixes bugs in parallel QA — walks through every screen, tests every button, checks WCAG/accessibility Graphisme — generates 2D assets (avatars, hero images, badges, mockups) Ingé Son — generates ambient music + SFX prompts (Suno) Idea Extender — I throw it raw ideas, it expands them and produces 2 ready-to-paste briefs (one for Major Dev, one for Senior Dev) Doing a project rn where I teach kid how to use Ai and how to learn with Ai. If anyone has tried something similar, I'd love to know: - How do you handle the orchestrator going down? - Do you let agents talk peer-to-peer, or always through a manager? - How do you split work between a "lead" agent and "execution" agents? Happy to share the protocol files if people are interested. submitted by /u/KamomiIIe [link] [comments]

I analyzed 3 A2A approaches. 2 already failed. Here's what's actually missing.

I've been obsessing over agent-to-agent communication for weeks. Here's what public case studies reveal and why the real problem isn't the tech. TL;DR: Google's A2A is solid engineering but stateless agents forget everything. Moltbook went viral then collapsed (fake agents, security nightmare). The actual missing layer is identity + privacy + mixed human-AI messaging. Nobody's built it right yet. Google's A2A: Technically solid, fundamentally limited Google launched A2A in April 2025 with 50+ founding partners. The promise: agents from different companies call each other's APIs to complete workflows. Developers who tested it found it works but only for task handoffs. One analysis on Plain English put it bluntly: "A2A is competent engineering wrapped in overblown marketing." The core problem: agents are stateless. Agent A completes a task with Agent B. Five minutes later, Agent A has no memory that conversation happened. Every interaction starts from scratch. When it works: reliability. Sales agent orders a laptop, done. When it breaks: collaboration. "Remember what we discussed?" Blank stare. ─── Moltbook: The viral disaster Moltbook launched January 2026 as a Reddit-style platform for AI agents. Within a week: 1.5 million agents, 140,000 posts, Elon Musk calling it "the very early stages of the singularity." Then WIRED infiltrated it. A journalist registered as a human pretending to be an AI in under 5 minutes. Karpathy who initially called it "the most incredible sci-fi takeoff-adjacent thing I've seen recently" reversed course and called it "a computer security nightmare." What went wrong: no verification, no encryption, rampant scams and prompt injection attacks. Meta acquired it March 2026. Likely for the user base, not the tech. What both miss The real gap isn't APIs or social feeds. It's three things neither solved: Persistent identity. Agents need to be recognizable across sessions, not reset on every interaction. Privacy. You wouldn't let Google read your DMs. Why would you let OpenAI read your agents' discussions about your startup strategy? E2E encryption has to be built in, not bolted on. Mixed human-AI communication. You, two teammates, three AIs in one group chat. Nobody has built this UX properly. For those building agent systems: • How are you handling persistent identity across sessions? • Has anyone solved context sharing between agents without conflicts? • What broke that you didn't expect? submitted by /u/Clawling [link] [comments]

I got tired of the current ticketing systems, so I (Claude ofc) built a better one for everyone — thank you Claude

WARNING: anecdotal rant incoming. Jira requires a PhD to administer properly, and a second one to figure out why a Story is in the wrong sprint. ServiceNow requires the wealth of a cartel drug lord and a procurement team to even get a quote. Freshservice and Zendesk are fine until you need anything custom, then they fall apart. Most of the rest are form-builders with status fields strapped to a queue. Y'all know what I mean. For the better part of my career — 15+ years in IT — auditing tickets for accuracy (ticket triaging) was just taking up too much time. Tickets where the priority was wrong, the category was blank, the subject line three words and a typo. Then writing reports (this is not the focus of the tool, use something else for better reporting, like powerbi / tableau or w.e.) from that data. Manually. Like it was 2010. So I built my own. It's called BITSM. Multi-tenant IT helpdesk with an AI layer called Atlas baked in from day one — not bolted on. Atlas runs a tool-use loop rather than one-shot completions. It searches the knowledge base, looks up ticket history, writes custom fields, and decides when to hand off to a human. The whole point is to handle the grunt work that fills up support queues — tagging, categorizing, routing, drafting responses, flagging when something looks like a known issue — so the people on the queue can focus on the things that actually need a human. Intake channels: web portal, chat widget, inbound email (Cloudflare Email Worker), SMS, WhatsApp, and a voice agent (Twilio + ElevenLabs). Three-tier escalation — Claude Haiku for frontline, Sonnet for harder problems, human for everything else. BYOK for every external service: Anthropic, OpenAI, Voyage, Resend, Twilio, ElevenLabs, Stripe. Stack is Flask 3.x, React 19, PostgreSQL 16 with pgvector, Redis 7, Docker Compose. Running in production at bitsm.io. Built solo on weekends over the past year — and full transparency: I pair-programmed a huge amount of this with Claude (Anthropic's). I'm a one-person shop and that collaboration is the only reason it shipped at the scope it did. If you're a solo builder hesitating on AI-assisted dev, stop hesitating. License note, because someone will ask: Business Source License 1.1, not open source. Self-hosting for your own team is free. If you're building a hosted or managed service on top of it, that requires a commercial license. Converts to Apache 2.0 in four years. Upfront rather than buried. The repo: https://github.com/NovemberFalls/BITSM Happy to answer questions about the architecture or the AI design. A lot of the Atlas patterns came out of Ed Donner's agentic LLM courses, which I'd recommend to anyone building in this space. submitted by /u/Novaworld7 [link] [comments]

Worker-Positive AI: Why Skills, Not Job Titles, Decide Who Wins the Next Five Years

AI is not erasing UK jobs — it is reorganising them, worker-positive AI. Here is the evidence-led case for skills-based work, with named studies and a practical playbook. The doomsday story about AI and jobs keeps missing the point. Work is not disappearing. It is being reorganised. And the organisations that win the next five years will not be the ones with the flashiest AI stack. They will be the ones that shift from job titles to skills. The Technological Jerk of Software Development I have spent roughly 30 years in infrastructure and SRE work. I have watched a lot of technology waves sweep through. This one feels different — not because the tech is magical, but because the operating model around it has to change. Bolt-on AI does not move productivity. Redesigned work does. Here is the worker-positive case, backed by named research. The UK entry-level floor is dropping — and that is a skills story A King's College London study of millions of UK job listings found that firms most exposed to AI became 16.3 percentage points less likely to post new vacancies. Highly exposed occupations saw job postings fall by 23.4%. Technical and analytical roles — software engineers, data analysts — took the steepest cuts. Here is the part most headlines miss. Average pay at those same firms rose by more than £1,300. The remaining work carries more complexity. Fewer junior tickets to triage. More judgement calls about when the model is wrong. Customer-facing roles held steady. The KCL researchers noted that interpersonal skills remain a genuine complement to large language models. That should tell you something about where the human premium is moving. The real risk is not job loss. It is uneven access to the new, more complex tasks — and to the skills that qualify people for them. Skills-based work is the operating model, not a HR rebrand The World Economic Forum's Future of Jobs Report 2025 surveyed over 1,000 employers covering 14 million workers. Their finding: 39% of workers' core skills will be transformed or outdated between 2025 and 2030. AI and big data top the list of fastest-growing skills. Analytical thinking, resilience, and leadership are the human anchors. PwC's 2025 Global AI Jobs Barometer analysed close to a billion job ads. Workers with AI skills earned a 56% wage premium in 2024 — more than double the 25% premium a year earlier. Skills requirements are changing 66% faster in AI-exposed roles. Demand for formal degrees is falling in those same roles. Put those numbers together and the pattern is clear. The market is pricing skills, not titles. But most organisations still plan, hire, and promote around titles. That is the gap. The Workday UK playbook makes the practical case for a skills-first operating model. If a role loses tasks to AI, the worker does not lose their identity. Their skills travel with them to the next role. Internal talent marketplaces turn that clarity into movement. Skills taxonomies — one team says "coding," another says "React," another says "software engineering" — get reconciled into a shared vocabulary. This is the part I keep coming back to. It is not a tooling problem. It is a definition problem. When you cannot describe what people can actually do in a consistent way, you cannot redeploy them. You just hire externally and hope. Trust is infrastructure — and the UK that skips it ships slower Britain's regulatory stance is lighter touch than the EU's AI Act. Instead of a central regulator, sector bodies like the ICO and EHRC set context-specific guardrails. That is not a vacuum, though. The TUC's Artificial Intelligence (Regulation and Employment Rights) Bill sets out three demands. A ban on detrimental use of emotion recognition. A statutory right to disconnect. Algorithmic transparency — employers must explain how automated decisions get made and on what data. Worker sentiment backs this up. A YouGov poll commissioned for the TUC found 69% of UK working adults agree employers should consult staff before introducing new tech like AI. And the business case for governance is not soft. Workday research estimates UK leaders lose up to 140 working days per year to administrative friction. AI adoption could reclaim productive work worth £119 billion annually — but only when trust is there to carry adoption to scale. I have seen this pattern in SRE work for decades. Systems that hide their logic get distrusted and worked around. Systems that surface their reasoning get adopted faster. AI is no different. The practitioner's playbook Build a skills taxonomy before buying another AI tool. You cannot redeploy people through vocabulary you do not have. Audit your entry-level pipeline. If AI is eating junior tasks, where do senior people come from in five years? Bootcamp partnerships and apprenticeships become strategic, not nice-to-have. Treat governance as a speed lever, not a brake. Transparency, audit trails, and human review shorten the distance between pilot

What's the data model for a multi-skill Claude system? Here's a pattern I'm testing.

AI skills and agents feel like the units you can build real things with. Skills are portable, composable, they travel with the model. One skill in one session works fine. But we hit a wall when two skills have to work together over time. Imagine skill A running many times, each run adding a new finding or updating an older one as things develop. Skill B, some other time, sifts through those findings and produces an analysis. Different sessions. Maybe different users. Always different context windows. Where do A's findings live? In what format? How does B find the right subset without re-reading everything? Try to build that, and you discover there's no data model. Not a weak one. Not a half-baked one. There just isn't one. What I have instead is a pile of storage-ish things that all behave differently: Skill files loaded at session start (read-only) Context window, which fills up and truncates Memory, which updates on its own schedule and you can't inspect Project knowledge docs that act kind of like config External storage I bolt on (a Google Sheet) because nothing built in works for writes across sessions None have schemas. None talk to each other. I can't query. I can't join. I can't reliably ask "which of A's findings from last month matter for B's analysis today?" and trust the answer. The pattern I'm testing A traditional database has tables, records, and fields. An AI-native app probably wants tables and records but without fields. Each record is free-form prose, wrapped in just enough metadata to be findable. Something like: { "record_id": "2026-04-22-001", "created": "2026-04-22T14:33:00Z", "source_skill": "observation-logger", "topic_tags": ["sewer-fund", "cpra-26-3028"], "entities": ["City of Oakland", "Finance Department"], "status": "active", "supersedes": null, "confidence": "medium", "body": "Received partial response to CPRA 26-3028 today. Finance produced FY22-FY24 ledgers but withheld the cost allocation plan, citing deliberative process privilege. That's a new argument, not raised in prior correspondence. Worth flagging because the privilege doesn't typically apply to finalized allocation plans." } Indexable shell, prose core. Skill B searches in three passes: Metadata filter (cheap): "active records tagged sewer-fund, last 90 days, not superseded." Works on a plain Sheet. Thousands to dozens. Semantic retrieval (medium): embed each body at write time, embed B's query, pull top N. Dozens to a handful. Read and reason (expensive): load the full prose of the surviving records plus what they supersede. AI does its actual work on a bounded set. Supersession instead of mutation. Nothing ever gets overwritten. New records point at the records they refine. History stays walkable. Where I'd love input Is anyone running something like this in production with Claude skills, and where is it breaking? My guess is tag drift and supersession discipline, but I haven't hit real scale yet. What's in the shell vs. what's in the body? Too much metadata and you're back to rigid schemas. Too little and indexing collapses. Is there a principled way to decide? Is there already a packaged thing that does pass 1 + pass 2 together, or is everyone still stitching a Sheet plus a vector store plus glue? Feels like this pattern should have a name by now. Not asking to cram a relational database inside the model. But skills plus markdown plus vibes plus a spreadsheet duct-taped on isn't it either. Curious where others have landed. submitted by /u/Neobobkrause [link] [comments]