Substack Notes AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

GitHub’s Fake Engagement Problem Is Hiding in Plain Sight

Turns out: very visible. Yesterday's scan found 185 out of 185 engagers on a single repo were bots. Not 90%. Not "mostly suspicious". Every single one. The repo had zero legitimate stars. What I built phantomstars is a Python tool that runs daily via GitHub Actions (free, no servers): Scrapes GitHub Trending and searches for repos created in the last 7 days with sudden star spikes Pulls star and fork events from the last 24 hours per repo Bulk-fetches every engager's profile via the GraphQL API (account creation date, follower counts, repo history) Scores each account on a weighted model: account age (35%), profile completeness (30%), repo patterns (25%), activity history (10%) Detects coordinated campaigns using timestamp clustering and union-find: groups of 4+ suspicious accounts that engaged within a 3-hour window Files an issue directly on the targeted repo so the maintainer knows what's happening Campaign IDs are deterministic SHA-256 fingerprints of the sorted member set, so the same group of bots gets the same ID across runs. You can track a farm across multiple days even as individual accounts get suspended. What the pattern actually looks like It's remarkably consistent. A fake engagement campaign in the raw data: 40-200 accounts, all created within the same 1-2 week window Zero original repositories, or only forks they never touched No bio, no location, no followers, no following All of them starring the same repo within a 90-minute window The target repo usually has a name implying it's a tool, hack, executor, or generator Today's scan: 53 active campaigns across 3,560 accounts profiled. 798 classified as likely_fake. The repos being targeted are mostly low-quality AI tools and "executor" software that needs manufactured credibility fast. Notifying the affected repo When a repo hits a 40%+ fake engagement ratio or a campaign is detected, phantomstars opens an issue on that repo with the full suspect table: account logins, creation dates, composite scores, campaign membership. The maintainer sees it in their own issue tracker without having to find this project first. Worth noting: a lot of these repos have issues disabled, which is a red flag on its own. Those get skipped silently. Why I built this Stars are how developers decide what to evaluate, what to depend on, what to recommend. When that signal is bought, it affects real decisions downstream. This started as curiosity about how measurable the problem was. The answer was more measurable than I expected. It's part of broader research into AI slop distribution at JS Labs: https://labs.jamessawyer.co.uk/ai-slop-intelligence-dashboards/ The fake engagement problem and the AI content quality problem are really the same problem. Fake stars are the distribution layer that gets garbage in front of real users. All open source. The data is append-only JSONL committed back to the repo after every run, queryable with jq. Repo: https://github.com/tg12/phantomstars Findings are probabilistic, false positives exist, the README explains the full scoring model. If your account shows up and you're a real person, there's a false positive process. Questions welcome on the detection approach, GraphQL batching, or campaign ID stability. submitted by /u/SyntaxOfTheDamned [link] [comments]

data engineering lead + solo consulting on the side. how claude restructured my client work. honest take.

amsterdam. 36. data eng lead at a B2B SaaS day job. side: solo data consulting practice. ~€4,800/mo on the side. 5 active clients. been using claude across both contexts for 10 months. wanted to share what i actually do because most "claude for data engineers" posts focus on coding. the bigger change for me was the non-coding work. what claude does in my workflow. client discovery. each new consulting client gets ~3 hours of upfront discovery. i used to do this in 1:1 calls and take notes. now i record (with permission), claude transcribes and structures. saves me ~90 min per client. i have a clearer picture of their tech stack and pain points than i used to. proposal writing. consulting proposals used to take me ~6 hours each. claude drafts 80% from the discovery transcript. i edit 20%. ~2 hours total now. ongoing client work. when i'm building a data pipeline for a client, claude is the rubber duck i talk to. i describe what i'm building, the constraints i'm running into. claude reflects back questions or alternate approaches. this has caught at least 3 designs that would have been wrong in the last 6 months. client deliverables. every engagement ends with a deliverable. used to be a 14-page word doc. now it's an ai product demo deck (built in Gamma, embedded data visualizations) the client can share with their team. clients keep these for years. project comms. weekly updates to each client. claude drafts based on my notes + git activity. i edit. ~20 min instead of ~90 min per client per week. the day job stack is similar but more technical. claude code for analysis tasks, sonnet via API for batch work, opus for the high-stakes architectural decisions. what claude doesn't do well in my workflow. debugging weird edge cases in production data pipelines. claude is good when the bug is a logic bug. claude is bad when the bug is "this specific data combination from this specific upstream system produces an unexpected result." those still need me to dig building from scratch in unfamiliar territory. if i don't have a mental model of what i'm building, claude can't substitute for the time i need to develop one. anything client-relationship. claude can write drafts. it cannot read a room. when a client is unhappy, claude makes the situation worse if i let it write the response. honest about cost. i pay ~€60/month for claude pro + a small api budget. side biz produces ~€4,800/month. roughly 1.2% of side revenue is going to claude. lowest-cost / highest-ROI tool in my stack by a wide margin. what i'd tell other technical people thinking about consulting on the side. claude makes solo consulting possible at the energy level you have after a day job. without claude i'd be doing maybe 1 client. with claude i'm doing 5. the math has changed in the last 18 months. submitted by /u/duskypetals56 [link] [comments]

wedding planner charleston. 4 years business owner. didn't expect claude to be the tool that changed my business this year.

charleston SC. wedding planner. 4 years. 18-22 weddings per year. average wedding budget $48k. team of 3 (me + 2 day-of coordinators). i don't usually post on this sub because i'm not technical. wanted to share because if claude is useful for a wedding planner in south carolina, it's probably useful for more service-business operators than the typical r/ClaudeAI audience. how i actually use claude. client comms. weddings involve emotional decisions. brides text me at 11pm asking about vendor concerns or family drama. before claude i'd respond in the morning and the bride would have been spiraling for 8 hours. now i type my rough response into claude at night, ask it to soften my tone (i'm direct, brides need warmth), and send the response immediately. response time per emotional message: 90 seconds. brides feel heard. nobody spirals overnight. vendor negotiations. emails to florists, caterers, photographers. i tell claude what i need to negotiate (price, change orders, scheduling conflicts) and the vendor relationship context. claude drafts a firm-but-warm version. i edit. send. saves me ~5 hours a week of vendor email i used to dread. timeline writing. each wedding needs a 14-hour day-of timeline. used to take me 6-8 hours per wedding. now claude takes my notes from the venue walkthrough + the couple's prefs + the vendor schedules and produces a draft. i edit. 2 hours instead of 6. proposal writing. when i'm bidding on a new wedding, claude drafts a proposal based on the consultation call. consistent quality. doesn't depend on whether i'm having a good week. emotional decisions, my side. i'm a wedding planner. clients have meltdowns. i absorb a lot. claude is my journal at the end of hard days. i type out what happened, what i'm feeling, what i should do differently next time. claude reflects back. it's not therapy. it's processing. what surprised me. claude works for non-technical service businesses. i'd been told by friends in tech that claude was "for coders." it's not. it's for anyone who writes things and makes decisions. it gives me back hours i didn't know i was losing. wedding planning is emotional labor as much as logistical labor. claude takes the logistical labor down significantly, which means i have more energy for the emotional labor that actually requires me. my brides notice. they don't know about claude. they notice that my responses are quicker, my timelines are more thorough, my emails sound warmer. they refer me to friends at higher rates than they did before. revenue impact (i tracked this carefully): 2024: ~$184k from 19 weddings. 2025: ~$247k from 22 weddings. partly more weddings. partly higher average wedding budget. some of it is claude. i'd guess 30-40% of the improvement is directly attributable to claude saving me time so i could take on better-fit clients. for other service business operators who think AI is "for tech people." it's not. open the app. talk to it about your business this week. report back here in 60 days. submitted by /u/Temporary-Prior7384 [link] [comments]

Small memory bridge for Claude Code skills that run as separate commands

I was testing a small pattern for Claude Code skills that run as separate commands. The problem: commands like /grill-with-docs, /tdd, and /handoff can be useful on their own, but they start fresh enough that you end up repeating the same project decisions. This example wraps a skill command and does a simple lifecycle: recall relevant Memanto memories before the skill runs inject them through MEMANTO_SKILL_CONTEXT run the skill command store durable notes from the finished run, such as decisions, conventions, caveats, and must/avoid rules The demo uses local JSONL by default so it can be reviewed without any API key. There is also a Memanto CLI backend for actual use. PR/diff: https://github.com/moorcheh-ai/memanto/pull/522 Curious if this feels like the right level of memory: explicit durable notes, instead of trying to summarize the whole chat every time. submitted by /u/dnesdan [link] [comments]

OpenAl Announced vs. Current Operational Compute

submitted by /u/Business_Garden_7771 [link] [comments]

Running multiple Codex sessions on macOS with separate app data

I recorded a short tutorial showing a macOS workflow for running multiple Codex sessions side by side, either with separated app data or with the same shared account. The first use case is separation. One Codex session for work, another for personal projects, and maybe another for experiments, without all of them sharing the same app state. For example, this can help keep a work account and a personal account separate instead of switching back and forth inside one shared app environment. I'm using a Mac app I built called Parall to create the launchers. It works with apps already installed on the Mac and creates independent launchers for them. The original app is not modified. There is another useful mode too. If a Parall shortcut is configured to not override the data path, it reuses the same account. That means you can have two Codex windows running the same account at the same time. This is useful when you have multiple tasks processing in Codex and want to watch them side by side. Inside the Codex app, you have to switch back and forth between tasks. With separate launchers, you can keep multiple active sessions visible at once, which can improve productivity. In the video, I show step by step how to create a separate Codex launcher that runs with its own data, then launch multiple Codex instances at the same time to show them working side by side. You can create and run as many instances as your Mac's RAM allows. When data separation is enabled, Parall creates a home-like structure inside the selected app data path. That folder can include symlinks that keep useful host configuration shared, for example SSH and Docker configs. This makes the setup flexible. You can remove symlinks or add new ones, so you control what is separated and what is shared between each Parall shortcut and the host. This is data separation, not full isolation. Each Codex instance can still access the same project folders on your Mac. This is not specific to Codex. Parall can also be useful with other AI coding tools and with most non-sandboxed Mac apps where separate app data or dedicated launchers are useful. Important notes: To run multiple Codex instances at the same time together with the original Codex app, the main Codex app must be launched first. To avoid that limitation, create multiple Parall shortcuts and use those shortcuts exclusively. I recommend disabling auto-update for all instances except one. Once that one instance updates Codex, restarting the other instances makes them use the latest update instantly. To log in to different accounts, close all Codex instances except the one you are logging in to. After logging in, you can run the instances at the same time. Curious how others are managing multiple Codex workspaces or accounts on macOS. submitted by /u/JulyIGHOR [link] [comments]

PrimeTask Bring Your Own AI - Claude sets up a full project in one prompt.

Hey r/ClaudeAI, I'm one of the developers behind PrimeTask, a local-first productivity system for macOS. The final beta now ships with Bring Your Own AI, a local MCP server (110+ tools, 5 prompt templates) so you can point Claude Desktop, Claude Code, Cursor, or LM Studio at it and let your own agent do the work. Quick demo in the video. One sentence from me, end-to-end project setup from Claude. What's happening in the clip I say I'm launching a Mac app in six weeks and ask Claude to set up the project. Claude creates the project with a deadline, three phase tasks (Design, Build, Launch) with staged due dates, descriptions, tags, subtasks, and short checklists. Sets a reminder on the first task so the native macOS toast fires during the recap. Recommends where to start. I say "start." Claude moves Design into the Design status and kicks off a timer. Twelve-plus tool calls under one prompt. No copy-paste, no manual setup. Why BYO AI (not a bundled cloud bridge) Server runs inside PrimeTask on your Mac. Your tasks, projects, CRM, and notes never leave the device. We don't ship a model. You bring your own: Claude Desktop, Claude Code, Cursor, LM Studio, anything MCP-compatible. No Anthropic-side context about your work. Claude only sees what your agent pulls in per turn. Per-space permissions: lock an agent to read-only or scope it to one workspace. Streamable HTTP with Bearer auth, or stdio if you prefer that route. Tool catalog profiles (Full, Core Tasks, Minimal, PrimeFlow, CRM, etc.) so smaller local models don't get drowned in 100+ tools. Five built-in MCP prompts (daily_standup, weekly_review, project_status, crm_summary, overdue_triage) for the workflows people actually want. Every tool call is logged in an in-app audit log. Full BYO AI docs (setup, transports, tool catalog, security): https://www.primetask.app/docs/integrations/bring-your-own-ai Why we built it this way Most "AI in your task app" is the app calling a vendor's API on your behalf, often with your data going through their pipes. We wanted the opposite. Your agent, your model, your machine. The app exposes a tool surface and gets out of the way. That's what BYO AI means here. PrimeTask itself is local-first, no account, no subscription, plain JSON on disk. BYO AI made the AI story consistent with that: nothing leaves your laptop unless you point your agent at one that does. Where we're at PrimeTask is wrapping up the final beta and heading to a stable launch this summer. Beta is now closed to new sign-ups. We're locking it down to ship the stable release. If you'd like to be notified at launch, drop your email here: https://www.primetask.app/notify or visit https://www.primetask.app Happy to answer questions about the MCP setup, the profile system, or how we structured the tool descriptions for agent discoverability. submitted by /u/XVX109 [link] [comments]

I built and shipped my Android app with Claude as my coding partner

Hi all I wanted to share a small win. I recently built and published my Android app, Nearfolks, and Claude was a big part of the development process. Nearfolks is a private relationship notebook for remembering people better. It helps users save notes about people, organize them into circles, set reminders, and remember small personal details before meeting someone again. The product idea was simple: not every relationship tool needs to be a sales CRM. Some people just want a private place to remember friends, family, community members, clients, and people they care about. The app is privacy-first: - no account - no cloud - no tracking - offline-first - data stays on the user’s device The app has a free version, and the upgrade is a one-time optional purchase for unlimited people, extra themes, and backups. No subscription. Claude helped me a lot with the build process: planning features, improving Flutter structure, debugging issues, writing cleaner code, thinking through edge cases, and getting unstuck during Play Console release problems. One release issue I faced was that closed testing worked fine, but production was blocked because of an older SQLCipher native dependency related to Android 16 KB memory page size support. Updating the dependency and rebuilding fixed it. What I found most useful about Claude was not just “write this code,” but using it like a patient technical partner: explaining errors, comparing approaches, and helping me move forward step by step. For people here who are building apps with Claude: - How do you structure your prompts for bigger projects? - Do you use Claude mainly for code generation, debugging, architecture, or product thinking? - Any tips for keeping an AI-assisted codebase clean as the project grows? Google Play: https://play.google.com/store/apps/details?id=com.nearfolks.notebook submitted by /u/shahzaib_sultan [link] [comments]

🏢 Andrej Karpathy Joins Anthropic - Returning to R&D and Pre-training

Andrej Karpathy, co-founder of OpenAI and former Director of AI at Tesla, announced on Monday that he is joining Anthropic. After focusing on AI education for the past two years via his startup Eureka Labs, Karpathy will now work within Anthropic’s pre-training unit under the leadership of Nick Joseph. Karpathy’s career has been central to major AI milestones, including a tenure at OpenAI (2015-2017) and leading Tesla’s Autopilot team until 2022. In January 2026, he famously identified a "phase shift" in software engineering, coining the term "vibe-coding" to describe the transition to agent-led development. He noted that AI coding agents crossed a critical coherence threshold in December 2025. This move follows a series of high-profile transitions from OpenAI to Anthropic, including co-founder John Schulman in August 2024. Karpathy stated that the next few years at the frontier of Large Language Models (LLMs) will be "especially significant," citing this as the primary reason for his return to active research and development. submitted by /u/andrewaltair [link] [comments]

I built ContextAtlas: A new take on context carry over and helps claude pick up new sessions where it left off in scope of your previous design decisions while saving your tokens avoiding rediscovery

When the "Build with Opus 4.7" hackathon was announced, I had been obsessing over the tokenomics of agents and how to make sessions go further without burning context on rediscovery work. We all have probably hit a session limit and wondered how it went so fast. I applied with that thesis, didn't get in, but I built it anyway over the last four weeks. I am proud to share that v1.0 ships today. Note up front: this is specifically a tool for development users. If you're using claude.ai web or Projects, ContextAtlas won't plug in directly. But if Claude Code is your main work flow or you utilize the Anthropic API, this tool was made for you. The pain: Claude Code learns your codebase fresh every session. "Where is OrderProcessor?" triggers a flurry of greps. "What depends on AuthMiddleware?" is another round of file reads. On a mid-sized codebase, an architectural question can burn 40+ tool calls and a lot of tokens before Claude has enough context to reason well. And the architectural rules in your ADRs and design docs? Claude has no path to those, so it confidently suggests changes that break constraints you may have documented elsewhere in your repo. What I built: ContextAtlas is an MCP server that pre-computes a curated atlas of your codebase (symbols, ADR-extracted architectural intent, git history, test coverage) and serves it to Claude Code in one call at query time in a smaller, token saving compact shape via a few lightweight mcp tools. Initial indexing happens once; querying is local and free. Example of what comes back when Claude calls get_symbol_context("OrderProcessor"): SYM OrderProcessor@src/orders/processor.ts:42 class SIG class OrderProcessor extends BaseProcessor INTENT ADR-07 hard "must be idempotent" RATIONALE "All order processing must be safely retryable." REFS 23 [billing:14 admin:9] GIT hot last=2026-03-14 TESTS src/orders/processor.test.ts (+11) Claude sees the idempotency constraint before proposing changes, not after a review catches the violation. https://i.redd.it/0ons3o28t32h1.gif Numbers: 45-72% token reduction on architectural prompts across three benchmark repos (TypeScript, Python, Go), with zero quality regression on measured axes. Full methodology and paired-t confidence intervals in the linked write-up. I wanted measurements, not vibes. Honest limits: single-judge model at v1.0 (cross-vendor panel is post-launch work). Quantitative claims bounded to three benchmark repos. Tie-bucket and trick-bucket prompts routinely show ContextAtlas net-negative; that's reported inline rather than buried. Install (two ways): In Claude Code: /index-atlas and /generate-adrs skills. No API key needed; runs under your subscription. Via CLI: uses Anthropic API for indexing. ​ npm install -g contextatlas contextatlas init && contextatlas index # then add the MCP server entry to your Claude Code config (snippet in the README) Both produce structurally identical atlases. Supported languages at v1.0: TypeScript (tsserver), Python (Pyright), Go (gopls), Ruby (ruby-lsp). Rust, Java, and C# are next on the roadmap; the adapter interface is small enough that they're realistic community contributions. What's next: v1.1 thesis is shaping up around developer onboarding flows and quality-validation work that was deferred from v0.8. And integrating external documentation of your code base into pre-indexing workflow. Full write-up: https://www.contextatlas.io/blog/v1.0.0 Repo: https://github.com/traviswye/ContextAtlas Also launching on DevHunt today: https://devhunt.org/tool/contextatlas; votes are very appreciated if you find ContextAtlas useful or an interesting approach. Built solo, hackathon-shaped scope, not pretending it's a full blown research paper, but did attempt to treat methodology as seriously. Happy to answer anything in the comments. Star the repo if you want to follow along, file an issue if it breaks for you on your codebase, and please be honest; this only gets better with feedback from people running it on real repos. submitted by /u/Kitchen-Leg8500 [link] [comments]

100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/

Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works. The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) 1. Write a Constitution, not a system prompt. A system prompt is a list of commands. A Constitution explains why the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. 2. Give your agent a name, a voice, and a role — not just a label. "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. 3. Separate hard rules from behavioral guidelines. Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. 4. Define your principal deeply, not just your "user." Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. 5. Build a Capability Map and a Component Map — separately. Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. 6. Define what the agent is NOT. "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. 7. Build a THINK vs. DO mental model into the agent's identity. When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. 8. Version your identity file in git. When behavior drifts, you need git blame on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. 🧠 MEMORY SYSTEM (9–18) 9. Use flat markdown files for memory — not a database. For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. 10. Separate memory by domain, not by date. entities_people.md, entities_companies.md, entities_deals.md, hypotheses.md, task_queue.md. One file = one domain. Chronological dumps become unsearchable after week two. 11. Build a MEMORY.md index file. A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. 12. Distinguish "cache" from "source of truth" — explicitly. Your local deals.md is a cache of your CRM. The CRM is the SSOT. Mark every cache file with last_sync: header. The agent announces freshness before every analysis: "Data: CRM export from May 11, age 8 days." Silent use of stale data is how confident-but-wrong outputs happen. 13. Build a session_hot_context.md with an explicit TTL. What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. 14. Build a daily_note.md as an async brain dump buffer. Drop thoughts, voice-to-text, quick ideas here throughout the day. The agent processes this during sync routines and routes items to their correct places. Structured memory without friction at ca

I gave Claude access to my M365 account using Power Automate + a small MCP server

I’ve been messing with MCP servers lately and finally got one working that feels genuinely useful instead of “cool demo, never use again.” The problem: I wanted Claude to be able to do basic Microsoft 365 stuff for me: read my inbox send a draft/follow-up check my calendar save notes into OneDrive make Planner tasks write rows into Excel fill a Word template But I don’t have tenant admin access, and I wasn’t going to get Graph permissions approved just for personal automation. The workaround was Power Automate. Every operation is a PA flow with an HTTP trigger. PA gives you a signed webhook URL. The flow runs as my account, using permissions I already have. Then I put a small FastMCP server in front of those webhook URLs and connected that to Claude. So now in a Claude chat I can say things like: “Email me a summary of this.” “What’s on my calendar tomorrow?” “Save this note to OneDrive under /Projects.” “Create a Planner task for this follow-up.” “Append this row to the tracking spreadsheet.” Under the hood Claude is just calling MCP tools like m365_send_email, m365_calendar_read, onedrive_create_file, etc. The MCP server posts JSON to Power Automate, and PA does the actual M365 action. The architecture is not fancy, defintely not: text Claude -> MCP tool -> FastMCP server -> PA webhook -> M365 connector I’m running the MCP server on a cheap VPS. It’s about 200 lines of Python plus a JSON config file of flow names and URLs. This was also a nice reminder that “agent tool access” doesn’t always need a perfect official API integration. Sometimes the janky enterprise tool you already have is enough. The funniest bug: I had two tools pointing at the same Power Automate webhook because I duplicated a flow and forgot to update the URL in my config. The result was Claude confidently calling the “right” tool and Power Automate doing the wrong damn thing. Very educational, not very dignified. Edit. A [you will probably need Power Automate Pro, which i needed for a couple other things) Here's an example of it. I built 22 Power Automate flows covering all the different tools that I would want called and then I added them to the mcp. In Power Automate, make one flow per action. Example: send email, read inbox, create calendar event, write OneDrive file, etc. Start each flow with “When an HTTP request is received.” Define the JSON body you want that flow to accept. For send email, maybe { "to": "...", "subject": "...", "body": "..." }. Add the normal M365 connector action. Example: Outlook Send Email V2, OneDrive Create File, Excel Add Row, Planner Create Task. End the flow with a Response action that returns JSON. Copy the HTTP trigger URL into a private config file. Do not commit it. Do not paste it anywhere public. Treat it like a password. Put a small FastMCP server in front of those URLs. Each MCP tool just validates the inputs, finds the right PA webhook URL, POSTs JSON to it, and returns the PA response. The wrapper is not fancy. It’s basically: AI tool call -> FastMCP function -> httpx.post(PA webhook URL, json=args) -> return response The main things I’d recommend are: - keep webhook URLs private - add a duplicate URL check at startup - log tool name + status, but not secrets - start with read-only tools before giving it send/write powers - make every flow narrow instead of one giant “do anything” endpoint. Will post more info in the am if needed. Thanks for reading! [If you are not familiar or not comfortable with Power Automate, what I would recommend (and I mean this sincerely) is to use either co-work or use Claude Code Terminal with the Chrome extension and plug in the prompt for it to do it. It's a little slow and it'll take a bit but it will make them. Just don't sit there and watch it if you want it to be quick.) submitted by /u/ChiGamerr [link] [comments]

Use Case: How I chain ChatGPT+Agents+Codex workloads

Context: I run interaction forensics and how people, communities, narratives, institutions and companies impact AI. Please note, all operations are human+AI. Summary: I have used digital forensic tools/OSINT in the past such as Maltego and wwanted a tool I could integrate with AI. So I built my own Airgapped. This tool is the first iteration and will later be used to assist in high-risk controlled environments such as child protection agencies. This is the current architecture and workflow. https://preview.redd.it/26w74lxfgz1h1.png?width=1935&format=png&auto=webp&s=4a064b2f5e84e230913f9e7758de2b29a1f41ac8 Tools Used and function: * Codex+Manus: Assistance in building the tool and incorporating logic. Bulk transfers of older method to current database. Data was collected by me and sorted into our database structure. * Agents: Amending and adding bulk data to database. * GPT+Manus: Verification and updates of data. The final output: Interface: https://preview.redd.it/t2x6v9l0iz1h1.png?width=1776&format=png&auto=webp&s=c1be628542af6420eb4efee9f7ec62c2d40146f9 Inferences and patterns identified when AI (LLM+AGENTS) review data. https://preview.redd.it/nkdio3z5iz1h1.png?width=832&format=png&auto=webp&s=01d0f0bc45e1968d0c692d712932f03e35969924 I add my own as well. Along with collaboration with AI to validate my understanding. Evidence based Artifacts: All knowledge is sourced and tagged https://preview.redd.it/fwcmjn28jz1h1.png?width=1253&format=png&auto=webp&s=861dcf33480d6e22919cf563a362c1c33c044734 These tie into a pattern identification graph so I can identify what may or may not be related. https://preview.redd.it/pegwypialz1h1.png?width=1424&format=png&auto=webp&s=d4b50e756354dc021fc106f5e91da3015ae0bd74 Would love any feedback for improvements. Please remember, the next iteration is for child protection where I intend to airgap a localised LLM with training corpora. The main idea is to MINIMISE users from having to review images and identify patterns/locations to expedite rescue. I want to add, this is also entirely self funded. I run a separate business to ensure I have funds for this and potential future hardware/licensing. submitted by /u/ValehartProject [link] [comments]

Personal tool for managing AI coding sessions across the board with some git features...

Started working on this last week since I found myself jumping vscode sessions, terminals and other windows too much and it cost a lot of time/mental energy finding sessions again where i left of or that need attention... Some key features: Multi-repo workspace — all your projects in one dashboard, not one window per repo Worktree-first — spin up a worktree per task/agent without losing track AI agent sessions built in — Claude Code, Codex, and other TUIs run inside the dashboard with live status Activity overview — see at a glance which sessions are working, waiting, or idle Unread badges + favicon alerts — know which session is waiting on you without tabbing through everything Sticky notes — pin thoughts to sessions, mention other sessions/files, build context without leaving the dashboard Custom per-session links — pin the Linear ticket, PR, or docs page next to the session Editor-agnostic — opens your existing editor, doesn't replace it Local-first — workspace is just a git repo on disk, no cloud required Could be OSS if there's interest... but right now it's really made for me and only tested on OSX (altough I try to keep crossplatform in mind since my other main dev machine is windows) submitted by /u/marwi1 [link] [comments]

Built a free Claude chat app with memory (Sonnet 4.5 is in there too)

The funny/painful timing here: I've been building this for months specifically because I wanted Sonnet 4.5 to remember everything. Then last week Anthropic pulled 4.5 from claude.ai. (I'm not a software engineer, just someone who cares a lot about AI and got obsessed with this problem and gets obsessed with things in general. Posting now because everyone seems to want sonnet back on chat and I have it.) Mneme runs on your own machine and talks to the Anthropic API directly. Because it's on the API, Sonnet 4.5 is still in the model picker. Honest catches first: The app is free. You pay Anthropic and OpenAI (for memory search) directly. Roughly $3 to $8/mo on Haiku for light use, $30 to $60 on Sonnet for moderate-highish use. No subscription. Tested mainly on Windows (one-click installer). Android browser access works over the local server/Tailscale, iPhone should work too. macOS is not packaged yet. Beta and solo dev. Things will break for someone and I'll be in the comments Setup takes about 10-20 minutes. The whole system is built non-technical people in mind, it should be relatively simple and intuitive to set up and use, and the GitHub page linked below has a PDF you can give to Claude to walk you through every step. What's actually in it (for the technically curious): There's no shortage of solid memory systems for Claude. Mneme isn't trying to win at codebase retrieval. It's a complete personal Claude client where memory is baked into the whole surface from the start, rather than added as a layer. That means: Tiered memory: Messages flow from episodic to narrative to entity summaries as relevance shifts; old context gets compressed without being lost. Daily summaries: A 7-day rolling timeline, so Claude knows what's been going on lately, not just what's semantically similar to the current message. Entity tracking: Hierarchical summaries built up over time for the people, projects, and things you keep referring to. Narrative concepts: Keyword-triggered recall for ideas you've named, surfaced when relevant. AI Notes: A persistent section Claude can write to itself between conversations. Extended thinking, file attachments, text-to-speech, a small command system (@run, artifact, etc.), autonomous python retrieval the AI can agentically use if automatic fails. Dynamic context: I wrangled with the Anthropic caching system for a while before I figured out a way to have every single message have different retrieval without breaking cache. Bon apppetit Open source (CC BY 4.0), local-first, all data in a SQLite database on your machine. It's aimed at the "journal with an AI" use case (thinking out loud, processing your week, having something that actually pays attention over time) rather than coding agents or RAG over docs. Link: Mneme-memory/MNEME-BETA: Beta version of the Claude conversational memory system Mneme (first big-ish public project, be gentle) (Video also made with Claude - shoutout to HyperFrames) (Model picker screenshot and architecture infograph in the comments if I can find a way to attach them) submitted by /u/iveroi [link] [comments]