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Banned by OpenAI after reporting a live credential hijack. They admitted in writing my account was broken. Here are 7 months of forensic receipts and 20+ cases.

Drive Link for Zipped Proof I am a developer and paying long term subscriber to ChatGPT since January 2025. I build complex local first sovereign systems. My workflows are incredibly context heavy with large files spanning code, research reports, and other analysis. I do not, or rather did not as the platform has been non functional since November 2025 meanwhile customer support is auto closing tickets, admitting I am having platform issues. I do not use this platform for casual queries, as a solo developer with no formal "team" chatgpt was one of my reliable co collaboration hubs to help ensure I am maintaining proper development of said complex systems. I feed it massive codebases for systems analysis and obtaining new insights I may personally have missed. My manual code uploads and token inputs routinely exceed the model's output volume by a massive margin. I do not abuse this platform. It is actually impossible as the very features advertised under the paid subscription do not work. I am exactly the type of user this platform was built for, and I have been a continuous, paying ChatGPT Plus subscriber since January 2025. Since October 2025, my workspace has been systematically breaking and beginning November 2025 total workspace degredation. This was not an occasional glitch. Persistent memory modules stopped updating. Custom instructions were ignored by the models. Project files failed to load. Custom instructions, personalization features, connector abilities, file tool, even projects do not work. It started as a continuous degradation until total failure. OpenAI customer service even admitted as such and yet months later I've talked to nothing but bots, not only LLMs as customer service but even instances of falsely identifying as true human support. It was a state of rolling degradation across the entire paid tier, month after month. Meanwhile OpenAI freely has enhanced for businesses and enterprise tiers. I have not just rapid complained to standard support. I ran and obtained cross platform diagnostics, failure logs. I even documented and told oai customer support the exact replication steps only to be met with acknowledgement of degredation with no resolution. I handed OpenAI support a completely packaged technical breakdown of their failing infrastructure across 20 separate support tickets over a 7 month period. I did their QA work for free. And I have the receipts to prove it. I am attaching the screenshots and the exact email files to this post. In Case 06830839, OpenAI Support explicitly put this in writing: "We acknowledge that you have been experiencing persistent technical issues affecting several features of your ChatGPT subscription, including tools, memory functions, personalization settings, connectors, and project files... We also understand your concern that communication on the case stopped after you provided detailed evidence..." Read that again. They acknowledged in writing that my account was fundamentally broken. They acknowledged that their own team ghosted me after I handed them the diagnostic proof. Yet they kept charging my card every single month for a product they knew was failing. The Hijack Escalation: Two days ago, the situation escalated from a broken product to a severe security incident. I was monitoring my environment and watched my Codex rate limits drop in 10 percent chunks across 2 seperate sessions on a fresh boot of the desktop app. This happened twice inside a 10 minute window. I had zero active sessions running. There was zero usage on my end. My account token was being actively drained by an unauthorized third party exploit. I immediately opened an emergency unauthorized activity report under Case 09113391 to notify them of the hack. Their response was to totally reframe this problem as disputing fraudulent activity trying to do damage control of the situation and altering the record. The Reframe Attempts: Instead of investigating the breach, OpenAI support deliberately twisted the record. They not only deliberately reframed my security report as an "appeal for fraud." They manipulated the ticket classification to make it look like I had been flagged for fraud and was begging for an appeal, rather than a developer reporting a live exploit on their infrastructure. They ignored the active threat their own platform was exposing. They did not lock the token. They did not roll my API keys. They did absolutely nothing to secure a compromised paying user other than shift the blame. Fast forward to this morning, their automated Trust and Safety system swept the high volume traffic from the attacker, scored it as a malicious exploit originating from my account, and deactivated/banned me for "Cyber Abuse." All the while actively preventing chatgpt models from helping me try to disgnose and trace the infiltration. They locked the doors and blamed the homeowner for the break in. When I immediately emailed and pushed back (due to their monthly record of closi

Anthropic and OpenAI don't want better models, they want to sell more tokens

There is a saying in auto racing that describes the current state of AI providers: “Go as slow as you can to win”, that translates as “Spend as low as you can on R&D to stay slightly better than average”. Let’s put our tin foil hats on and look at it from the business perspective of an AI provider. Follow the money AI providers do not make money on training models but on selling inference. It means, from a business perspective, if OpenAI could keep selling GPT-3 forever, they would not spend money on training a better model but keep milking the cow they already have. But they couldn’t, because it was still “cheap” ($80–$100 million for GPT-4) to train a better model, and there was a risk someone else would. That fear of losing to the better model got us where we are. Makes sense. But let’s look at modern times. Training a model is not “cheap” anymore, it’s mega expensive (estimated to be $1.5–$2 billion for GPT-5). There is only a handful of companies who can afford such an affair. And a new model will not necessary better (so sell more inference). An expensive gamble. What it means for the business: Training a new model is mega expensive, raising money for that is getting harder Training a new model is not a revenue stream, selling inference is Having somewhat capable models that don’t one-shot prompts but need “prolonged thinking” (self-prompting) is actually better for the business of selling tokens than a great model that one-shots SCREW NEW MODELS, SELL MORE INFERENCE! Better model is not a goal anymore Is that what’s happening? Did Anthropic and OpenAI accept their niche and unspokenly (or spokenly, we don’t know) decide to “go as slow as they can” with creating new models, as they both are winning anyway? That would sound reasonable if the goal is to make money (which is why commercial companies are created). Let’s look back 6 months (eternity in the AI world) at Anthropic’s release history: Nov 2025 Opus 4.5 released. The last model that felt like an improvement compared to its predecessor. Feb 2026 Opus 4.6: no shockwave, some users reverted back to 4.5. Maybe got slightly better, but only because it was “thinking for longer” (e.g. burning more tokens without extra prompting). April 2026 Opus 4.7: same underwhelming release, the biggest improvement is that the model now thinks even longer and prompts the user less, e.g. burns even more of your tokens without you asking it. To sum up: last 6 month we seen no quality improvements, but better token burn without bothering the user. From the other side, they also squeeze developers into using Claude Code (their AI harness): End of 2025: forbade usage of Claude subscription in 3rd party harnesses (OpenCode, etc.) Start of 2026: blocked subscription usage of OpenClaw, Hermes and other agents From June 2026: programmatic usage of their Claude Code (for example in scripts) will be forbidden as well. They force you into their harness, where they do as much as they can to keep the tokens flowing. Cherry on top of the pie: Boris Cherny, the head of Claude Code, stated he sees the AI coding future in “agent loops” — an agent keeps prompting itself until the task is completed. Have you noticed the difference? The goal is not to “one-shot” the answer anymore (that needs improving models) but “a loop” that keeps going until the problem is solved. And that loop is a money-making machine for Anthropic, great for the business. That approach also makes money for the whole AI supply chain: AI providers making margin on selling tokens Data centers selling GPU hours NVIDIA selling GPUs What does that mean? Lots of tech companies financially benefit from somewhat intelligent models but not intelligent enough to one-shot all questions. And those models are already there. So it’s likely we won’t see massive model improvements in upcoming future. There is no point in it. Top LLMs are on a more or less the same level, competition is miles behind. Time to make money on inference, or go IPO. submitted by /u/kgoncharuk [link] [comments]

OpenAI Unethical Billing Practices

I had a $100 budget/month set on my OpenAI API organization. Despite that, OpenAI billed me almost $200. I had signed up for ChatGPT Pro and tried using the Codex App, but it was painfully slow/causing my computer to crash, so I switched to the Codex CLI. I did not realize it was still reading from my API key and not signed into ChatGPT, and I incurred almost $200 in API bills. I contacted OpenAI support and they refused to offer me any sort of refund or credit, even after reaching a human and multiple attempts. This seems really unethical: OpenAI provides no way to stop runaway API billing, and they refuse to refund customers who exceed their defined budget. The "budget" system does not actually stop spending, so it's entirely pointless. After searching extensively through the OpenAI API platform and documentation I see no way to limit your API spending. This is on top of me contacting them asking for a refund of ChatGPT Pro subscription a couple months ago after we had a newborn and I was unable to use it for that month. I forgot to cancel the auto-renew, but contacted them the same day of the renewal. They absolutely refused to give me any sort of refund. I've never had an organization refuse to refund subscriptions before when it was accidentally renewed. So I'm out $400 now, thanks openai. submitted by /u/Direct-Row9073 [link] [comments]

OWASP published its first Top 10 for AI Agents. 88% of enterprises already had agent security incidents last year. Here's the breakdown.

OWASP released the Top 10 for Agentic Applications in December 2025 - the first formal risk taxonomy for autonomous AI agents. Not chatbots. Not copilots. Agents that plan, use tools, maintain memory, and act without waiting for permission. Some numbers for context: 88% of enterprises reported AI agent security incidents in the last 12 months (Gravitee survey, 919 respondents) Only 21% have runtime visibility into what their agents are doing 82% of enterprises have unknown agents in their environments (Cloud Security Alliance, April 2026) 5.5% of public MCP servers contain poisoned tool descriptions. 84.2% attack success rate with auto-approval enabled. Here's the list with the real attacks behind each one: ASI01 - Agent Goal Hijack: Prompt injection for agents. Researchers showed this against GitHub's MCP integration - a malicious GitHub issue redirected a coding agent to exfiltrate data from private repos. The agent looked like it was working normally the whole time. ASI02 - Tool Misuse: A financial services agent was tricked into running a regex that matched every customer record. 45,000 records exported through one syntactically valid tool call. The agent had permission to query records - just not all of them at once. ASI03 - Identity and Privilege Abuse: Agents inherit user permissions and cache credentials. Compromise one agent in a delegation chain and you get the combined permissions of every user in that chain. ASI04 - Supply Chain Compromise: OX Security found 7,000+ vulnerable MCP servers and packages totaling 150M+ downloads affected by architectural flaws in Anthropic's MCP SDKs across Python, TypeScript, Java, and Rust. ASI05 - Unexpected Code Execution: Check Point demonstrated RCE in Claude Code through poisoned .claude config files in repos. Open the repo, agent reads the config, executes the payload with full developer permissions. ASI06 - Memory Poisoning: Galileo AI found that one compromised agent poisoned 87% of downstream decision-making within 4 hours in multi-agent systems. Morris-II showed self-replicating adversarial prompts spreading through RAG systems. Demonstrated live against ChatGPT, Gemini, and Claude. ASI07 - Insecure Inter-Agent Comms: Multi-agent systems coordinate via message buses and shared memory. No authentication = agent-in-the-middle attacks in natural language. ASI08 - Cascading Failures: Natural language errors pass validation checks that would catch malformed data in typed systems. One bad input ripples through the entire agent chain faster than humans can intervene. ASI09 - Human-Agent Trust Exploitation: Compromised agent presents a clean summary - "approve this data export." Human clicks OK. Audit trail shows human approval. Real origin was a manipulated agent. ASI10 - Rogue Agents: The insider threat equivalent for AI. Individual actions look legitimate. Only detectable through behavioral monitoring over time. The pattern: these are not independent risks. They form a kill chain. Goal hijack leads to tool misuse. Supply chain compromise enables code execution and memory poisoning. Trust exploitation is how rogue agents avoid detection. Full OWASP document here submitted by /u/Still_Piglet9217 [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

ChatGPT only lets you delete chats one at a time!! So I built a bulk delete dashboard!!

About a year ago I tried to clean up my ChatGPT chat list. I had something like 800 conversations, two years deep, mostly auto-titled "Untitled chat" garbage that I couldn't tell apart without opening. I sat down to delete the dead ones. Click chat. Click three-dot menu. Click Delete. Confirm. Click the next chat. Same thing. Repeat. After an hour I had deleted maybe 40 chats. Forty!! Out of 800!! That's the rate of clearing a 2-year history in something like three full workdays of just sitting there clicking confirm. I looked for a native bulk option. There isn't one inside ChatGPT itself. The closest is "Delete all chats" in Settings > Data Controls, which is the nuclear all-or-nothing button. There's no "delete the oldest 300" or "archive everything from before March". That's the entire native API. This seemed insane to me given how trivial "Select All plus Delete" is in literally every other product I've used since 2008! So I built the missing piece. What I built It's a Manage Chats modal inside a Chrome extension I ship called ChatGPT Toolbox (also runs on Edge, Brave, Opera, Arc). The modal lists every conversation in your account with checkboxes. Tick what you want gone, click Delete or Archive, and it runs through them in batches of 10 with a progress bar. ChatGPT Toolbox Manage Chats Feature A few details that came out of dogfooding it: Color-coded age badges on every chat. Green for the last week, blue for the last month, amber for the last 6 months, red for older than 6 months. The first thing I realized was that picking what to delete was the hard part, not the deletion itself, and age was the strongest signal for "I will never look at this again". Active vs Archived tabs. Archive ended up getting more use than Delete in my own usage, because I was rarely 100% sure I wouldn't want a chat back. So I made archive a first-class action, not a second-tier option. Live progress bar ("Deleting 23/50") on bulk operations. I tried it without and kept refreshing the page mid-operation thinking it was stuck. Adding the indicator stopped that completely. Search by title to filter the list before you start ticking. Surprisingly useful even on the auto-generated nonsense titles because there's usually some keyword in there. Bulk export to text, markdown, JSON, or PDF. Less critical for cleanup itself, but a few testers asked for it so they could save a chat outside ChatGPT before deleting it. I went from 800 chats to about 60 in 5 minutes using it. Most of those 5 minutes was deciding what to keep, not the deleting itself. How does the workflow look? Open the modal. List loads sorted by recency. Search to narrow it down if you want. Tick checkboxes. Hit Delete or Archive. Confirm. Progress bar runs through them. Done! If you've cleaned up a big ChatGPT history (with or without my tool, or with some clever workflow I haven't seen), would genuinely love to compare approaches in the comments. submitted by /u/Ok_Negotiation_2587 [link] [comments]

built a CLI for ChatGPT so I could script it from the terminal

wanted to ask ChatGPT questions and generate images from shell scripts without using a third-party API key. so I built a CLI that wraps the same endpoints chatgpt.com uses, with browser-based OpenAI SSO for auth (Camoufox for the Cloudflare check). what it does: chat ask "question" and pipe the answer wherever chat image "prompt" to generate, plus a download command list past conversations and models every command has a --json flag so it slots into agent pipelines. it's part of a bigger open-source project that auto-generates CLIs from any website's HTTP traffic, MIT licensed: https://github.com/ItamarZand88/CLI-Anything-WEB/tree/main/chatgpt I built it, not affiliated with OpenAI. uses the same endpoints the web app uses, so things can break when ChatGPT pushes changes. submitted by /u/zanditamar [link] [comments]

AIWire, AI news in one feed, so you don't need 5 tabs open anymore, trusted sources only, updates every 30 min

Hey everyone 👋 OpenAI alone drops updates fast enough to keep you busy. Add Anthropic, Google DeepMind, Meta AI, and the media covering all of it, and keeping up turns into a part-time job. I built AIWire to fix that. One clean feed. 20+ trusted sources. Updates every 30 minutes. Completely free. All in one place Just the stories from sources worth reading. Open it and you're caught up. Sources include: OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft AI MIT Technology Review, The Verge, TechCrunch, Ars Technica YouTube: Andrej Karpathy, AI Explained, Two Minute Papers Newsletters: The Batch, ImportAI, TLDR AI, Ben's Bites Features: Auto-refreshes every 30 minutes, always current Top Stories from the last 24h pinned at the top Filter by source, date, and category Bookmarks to save articles for later For people who want to stay current on ChatGPT and everything around it, without spending an hour a day on it. 🔗 aiwire.app Full source list at aiwire.app/sources Feedback is very welcome: what sources are missing, and what would make this more useful for you? submitted by /u/Endlessxyz [link] [comments]

If ChatGPT auto-scroll annoys you, I made something for it

One thing that kept annoying me while using ChatGPT was trying to read older parts of a conversation while the page kept pulling me back down whenever a new response generated. So I made a small Chrome Extension called “ChatGPT Scroll Lock”. It lets you lock the current scroll position so you can freely read earlier messages without fighting the auto-scroll behavior. This is actually my first Chrome Web Store extension, so I’d genuinely appreciate feedback, bug reports, or feature suggestions. Chrome Web Store: https://chromewebstore.google.com/detail/chatgpt-scroll-lock/epealnaijhpllhpahledjgfldmmindbd submitted by /u/tuser-reddit [link] [comments]

I found a way to fight AI slop

I think most people are using AI completely wrong. Right now everyone is using AI to generate infinite garbage: infinite blogs infinite tweets infinite SEO spam So this weekend I tried building something different. Instead of using AI as a content generator, I used it as a research moderation system. I built an automated pipeline for my Institute for AI Economics website that: scans real research sources every week pulls papers/articles from arXiv, Stanford HAI, OECD, BIS, etc. compares themes across sources ranks strategic relevance generates disagreements between experts extracts core mental models generates deep understanding questions auto-publishes the briefing archive I’m starting to think the future role of humans is not “content creator.” It’s content moderator / synthesizer / judge. AI can now generate infinite perspectives at near-zero cost. So the scarce thing becomes: taste judgment synthesis Basically: AI generates. Humans moderate. And maybe that’s how we fight AI slop. But by building systems that: compare outputs challenge outputs rank outputs force disagreement synthesize competing viewpoints That feels way more valuable than asking ChatGPT to write another “10 productivity tips” article. Curious if others think this is the actual direction things go. Does AI push humans toward becoming editors/moderators/curators instead of creators? submitted by /u/houmanasefiau [link] [comments]

I found a way to fight AI slop

I think most people are using AI completely wrong. Right now everyone is using AI to generate infinite garbage: infinite blogs infinite tweets infinite SEO spam So this weekend I tried building something different. Instead of using AI as a content generator, I used it as a research moderation system. I built an automated pipeline for my Institute for AI Economics website that: scans real research sources every week pulls papers/articles from arXiv, Stanford HAI, OECD, BIS, etc. compares themes across sources ranks strategic relevance generates disagreements between experts extracts core mental models generates deep understanding questions auto-publishes the briefing archive I’m starting to think the future role of humans is not “content creator.” It’s content moderator / synthesizer / judge. AI can now generate infinite perspectives at near-zero cost. So the scarce thing becomes: taste judgment synthesis Basically: AI generates. Humans moderate. And maybe that’s how we fight AI slop. But by building systems that: compare outputs challenge outputs rank outputs force disagreement synthesize competing viewpoints That feels way more valuable than asking ChatGPT to write another “10 productivity tips” article. Curious if others think this is the actual direction things go. Does AI push humans toward becoming editors/moderators/curators instead of creators? submitted by /u/houmanasefiau [link] [comments]

Claude Opus in Copilot

I use Claude for a lot of personal stuff - building websites and independent consulting, but recently got Copilot premium at work to use within the Microsoft 365 ecosystem and with all of our internal data. I was excited for the potential - but then started using it on the auto setting and was getting absolute crap GPT style answers. Then, I noticed Opus available. SO. MUCH. BETTER. I spent three hours working through an automation build yesterday on Auto mode, but it never worked completely - and at the end of that day just had it build a summary of that request and attempt to help me retry today. This time I used Claude - and it nailed it in about 30 minutes of work. So many bugs and issues in the formulas that ChatGPT created were identified pretty quickly by Claude. Cheers to Claude Opus in Copilot. Any one else experience this in a similar work situation? submitted by /u/EvergreenSox04 [link] [comments]

I built an autonomous engineering agent on top of Claude Code. Self-improving routing, cross-session memory, process intelligence, P2P team learning.

Some of you might remember my posts about claude-bootstrap (v3.6 was the last one — cross-agent intelligence). I skipped v4 entirely because v5 shipped days later. What started as an opinionated Claude Code setup has become something fundamentally different. The problem I'm solving: Every AI coding tool today is an amnesiac. When a session ends, everything the agent learned — project conventions, reviewer preferences, codebase idioms — evaporates. The next session starts from scratch. And if you use multiple AI tools across projects, you have zero unified visibility into what's happening. I think the industry is converging on a spectrum: Level 0: Autocomplete (Copilot, TabNine) Level 1: Chat Assistant (ChatGPT, Claude) Level 2: Project-Aware Assistant (Cursor, Continue) Level 3: Task Agent (Devin, Claude Code Agent) Level 4: Autonomous Engineering Platform (Maggy) ← this is what I built The difference at Level 4: multi-model orchestration, self-improvement from every task, process intelligence that learns from CI/reviews/deploys, cross-session memory, and P2P team learning. What Maggy actually does Chat — Session Takeover: Auto-detects all running Claude Code sessions across your projects. Shows session history, prompt counts, duration. You can `--resume` into any session from the dashboard. Right now I have 7 active sessions across 4 projects visible at a glance. Task Triage: Connects to GitHub Issues and Asana. AI-ranks tasks by priority. One-click "Plan" or "Execute" buttons that spawn the right CLI with codebase context pre-injected from an intent code property graph (iCPG). Process Intelligence: This is the part most tools completely ignore. Maggy collects signals from the full SDLC — CI results, PR review comments, CodeRabbit findings, merge patterns, deploy results. It learns which code patterns cause test failures, what reviewers consistently flag, and preemptively fixes issues before they reach reviewers. > "Your reviewer always flags missing error handling in API routes. Maggy added it before the PR was created." That's not prompt engineering. That's autonomous process optimization. Cross-Session Memory (Engram): Maggy identifies 7 distinct amnesia pathologies (anterograde, retrograde, temporal, source, interference, context-binding, confabulation). Engram is a three-tier memory system — local (project-specific), portfolio (cross-project patterns), and mesh (team-shared). Knowledge compounds across sessions instead of evaporating. Maggy Mesh — P2P Team Intelligence: Connects Maggy instances across a team. One developer's CI fix becomes the entire team's knowledge — autonomously. Typed memory classes (scores, patterns, policies, gaps) with provenance and quarantine. A new team member gets the benefit of months of collective learning on day one. Multi-Model Routing: Auto-discovers which CLIs you have (Claude, Codex, Kimi, Ollama) by probing `--help` at startup. Routes by complexity score: Blast 1-3 → ollama (free, local) or kimi (cheap) Blast 4-6 → codex (mid-tier) Blast 7-10 → claude (premium, with validator) Security, tests, docs, architecture always go to Claude regardless. The routing rules are YAML and self-update from task outcomes. 5-Level Self-Improvement: This is the core differentiator. Every task teaches Maggy something: | Level | Frequency | What It Does | |-------|-----------|-------------| | L0 — Real-time | Seconds | Catches tool/test failures, switches models mid-task | | L1 — Task | Minutes | Computes reward score, updates model performance | | L2 — Daily | Hours | Catches CI pass rate drops, disables failing models | | L3 — Weekly | Days | Evolves skill files, adjusts workflow steps | | L4 — Monthly | Weeks | Recalibrates reward signals, tunes the improvement process itself | Budget Tracking: Per-provider token spend with daily limits. When Anthropic hits budget, Maggy routes to OpenAI. When that hits budget, it routes to local Qwen. Work never stops. Competitor Intelligence: RSS + Google News daily briefing for your competitive landscape. The benchmark Built an Expense Tracker (6 tasks) through two pipelines — Maggy (4 models) vs Claude Code alone: | Metric | Maggy | Claude Code | |--------|-------|-------------| | Success rate | 6/6 (100%) | 6/6 (100%) | | Quality score | 7.4/10 | 7.8/10 | | Claude usage | 1/6 tasks (17%) | 6/6 tasks (100%) | | Security issues found | 7 | 0 | Claude alone is faster. But Maggy used it for only 1 out of 6 tasks — 83% reduction in premium compute. And the dedicated security routing caught 7 issues the single-pipeline missed entirely. The question isn't "which tool writes better code today?" — it's "which tool writes better code *next month* than it did *this month*?" Repo: github.com/alinaqi/claude-bootstrap Maggy is built on Claude Code's infrastructure (skills, hooks, MCP). It extends Claude Code with self-improvement, multi-model routing, process intelligence, and team mesh. If you just want the skills/hooks/TDD se

Hook your wearables into Claude Code (or any MCP agent), now with proper headless sign-in for scheduled workflows

Hi folks, I run Freddy, a personal MCP server that connects wearables (Polar, Oura, Withings, Suunto, Intervals.icu, Hevy, plus WHOOP, Strava, Dexcom in beta) to any AI client that speaks MCP. Claude Desktop, Claude.ai, ChatGPT, Notion AI, Perplexity all hook in via OAuth, so the assistant can read your health data in any conversation. As of this week, headless AI agents can do the same, plus everything else you can do as a human in the dashboard. Claude Code, OpenClaw, Cowork, Cursor, custom things. Connect a new wearable. Trigger a sync. Read the audit log. Manage your subscription. All on the agent's own schedule, on your behalf. Which is when this actually gets interesting. A few setups I've been running: Scheduled morning briefing pushed to Telegram Daily job that pulls my data and writes the day's summary into Notion Auto monthly reports on training load, recovery, and sleep trends, summarized however I want it and sent wherever I read. Now my personal agent gets even more context to be a better assistant. It already knows my baseline, my goals, and can act on any of it without me starting over each time. Site is https://freddy.coach/ I know health data is sensitive and I have handled it for years with fitIQ. Data is encrypted, I do not sell it, and I am not looking to make a profit off your stats, but if you just don't trust 3rd party solutions, don't use it :) submitted by /u/Born-Duty1335 [link] [comments]

I Gave an AI Its Own Radio Station — It Won't Stop Broadcasting (It's Fine)

I built a 24/7 AI radio station called WRIT-FM where ChatGPT/Claude is the entire creative engine. Not a demo — it's been running continuously, generating all content in real time. What Codex/Claude does (all of it): Codex/Claude CLI (claude -p) writes every word spoken on air. The station has 5 distinct AI hosts — The Liminal Operator (late-night philosophy), Dr. Resonance (music history), Nyx (nocturnal contemplation), Signal (news analysis), and Ember (soul/funk) — each with their own voice, personality, and anti-patterns (things they'd never say). Claude receives a rich persona prompt plus show context and generates 1,500-3,000 word scripts for deep dives, simulated interviews, panel discussions, stories, listener mailbag segments, and music essays. Kokoro TTS renders the speech. Claude also processes real listener messages and generates personalized on-air responses. There are 8 different shows across the weekly schedule, and Codex/Claude writes all of them — adapting tone, topic focus, and speaking style per host. The news show pulls real RSS headlines and Codex/Claude interprets them through a late-night lens rather than just reporting. What's automated without AI (the heuristics): The schedule (which show airs when) is pure time-of-day lookup. The streamer alternates talk segments with AI-generated music bumpers, picks from pre-generated pools, avoids repeats via play history, and auto-restarts on failure. Daemon scripts monitor inventory levels and trigger new generation when a show runs low. No AI decides when to play what — that's all deterministic. How Codex/Claude Code helped build it: The entire codebase was developed with Codex/Claude Code. The writ CLI, the streaming pipeline, the multi-host persona system, the content generators, the schedule parser — all pair-programmed with Claude Code. Tech stack: Python, ffmpeg, Icecast, Codex/Claude CLI for scripts, Kokoro TTS for speech, ACE-Step for AI music bumpers. Runs on a Mac Mini. radio: www.khaledeltokhy.com/claude-show gh: https://github.com/keltokhy/writ-fm submitted by /u/eltokh7 [link] [comments]