https://youtube.com/shorts/9ywqgu3R_mw?si=EjQzEF-xiB1rYRcy

Look what ChatGPT says after reading the Terms of Service of JEV .. 😬 what do you think? TYPE SAFE CONTRACT REVIEW — MAIN RED FLAGS If this service will handle confidential, regulated, or customer-facing data, I would not sign these terms unchanged. • DATA DELETION TypeSafe can delete Customer Data at any time. The agreement provides no guaranteed export window, transition period, or deletion schedule, and allows retention in backups. Ask for a defined export period, deletion timeline, and backup-purge process. • BROAD TELEMETRY RIGHTS TypeSafe may use Customer Data indefinitely to create “Telemetry,” including hashes, classifications, metrics, and “learnings.” It may then use Telemetry without restriction to improve other products. Ask that Telemetry be aggregated, deidentified, and non-reidentifiable. • VERY LOW LIABILITY CAP Liability is capped at the greater of 12 months’ fees or $50. Lost data, security failures, and consequential damages are excluded. That is especially unfavorable for production use or sensitive data. • BROAD CUSTOMER INDEMNITY The customer indemnifies TypeSafe for claims involving Input, the customer’s application, license violations, and essentially any End User claim related to the service. This should be narrowed, made mutual, and limited to third-party claims caused by the indemnifying party’s breach or negligence. • NO CLEAR PROTECTION FOR AI OUTPUT TypeSafe assigns Output to the customer but excludes Output from its IP indemnity. The customer may therefore bear much of the risk that generated output infringes someone else’s rights. • WEAK SERVICE COMMITMENTS There is no meaningful uptime SLA, support-response commitment, recovery objective, or strong remedy for outages. TypeSafe may also change the API in ways that make the customer’s application incompatible. • CREDIT AND PRICING RISK Credits can expire, are nonrefundable, and may be consumed at changing rates. Automatic refills can also create additional charges. Ask for spending limits, advance notice of rate changes, and no automatic refills without separate approval.

Hi everyone, For those who have used both Claude Code and Codex through their subscription plans, is Codex actually better and faster for vibe coding? I have been using Claude Code on a subscription for a while. It is often very capable, but I have found it extremely slow. I switched from the Mac desktop app to VS Code, hoping it would improve the experience, but many tasks still take a very long time. Lately, I have also noticed more mistakes and patches that frequently break things. I am now spending more time debugging and repairing its changes than making progress on my projects. I have tried several Claude models, including Fable 5, Opus 5, Opus 4.8, and now Fable 5.1. I have heard that Codex is faster and more efficient, but I would appreciate clarification from people who have used both: 1. Can Codex be used through a subscription, such as the $200 ChatGPT plan, with coding limits comparable to Claude Code? Or does extensive use require separate API billing? 2. Is Codex genuinely faster and more reliable than Claude Code for vibe coding? 3. Can Codex take over projects that were started in Claude Code by reading the existing codebase, `CLAUDE.md`, and other Claude project files? I would especially appreciate feedback from anyone who has used both platforms extensively on real projects. Thank you so much for your help.🙏

This is happening to me all the time now, and either I wasn't catching it before, or something changed. I do realize that this might be operator error, which is why I'm asking for help. Have you been able to wrangle the "hallucinations" which let's just call them what they are, fiction and/or lies. One request: please don't blindly copy/paste outputs if you run this post with AI. I'm not saying don't do that, just politely asking if you could give it a read and if the output makes sense to you, drop it in the comments. Here are two examples: --------------- Example #1 --------------- Had Claude draft this up, and in the process it created a rule for it without me asking. What's frustrating about this is that now I have to read through another markdown file to ensure accuracy and consistency to how I do things, and I still have doubt that it'll even honor the rule it wrote for itself. The admission: 1. What I did. Credited three voice rules to NAME, and wrote that he flagged them in a comment on your Voiceprint LinkedIn post. The comment doesn't exist. I checked his comments feed, his posts, Gmail, Slack, and Skool. The earliest trace is my own /session capture on 8/29, with nothing upstream of it. I can't reconstruct where the name came from, and I didn't invent a story for how I invented it. 2. It happened twice. Yesterday I told you the attribution had reached the public repo. It hadn't. I repeated the session log's "added all three to..." line without opening the file. That second one is the more useful failure, because it shows the actual mechanism: an unsourced sentence in a workspace file gets read as a source by whoever comes next, including me. 3. Six guardrails were loaded and all six missed. constraints.md bans invented attributions by name. Its final scan says every claim traces to a source. Shared rule 0 forces that file to load every EOD run. Root map rule 5 says verify the live system. Digirol bans "I think" and demands a real search before calling a contact new.

For a long time, my biggest AI problem was not a lack of models. It was fragmentation. My knowledge was scattered across ChatGPT, Manus AI, Perplexity, Claude Code, Hermes Agent, and other systems, with each one effectively operating as its own island. I had tried to unify everything inside an Obsidian vault using a Karpathy-style flat wiki structure, but it was still fundamentally static knowledge management rather than a true contextual memory system. Then my CMO told me, "John, you should watch @Jake Van Clief 's video. He explains how to do this simply." I watched one ICM explainer, immediately went down the rabbit hole, joined this community, read the ICM paper, and started building. 📚 And for context, I'm a doctor with no coding background. 😂 I started vibe coding because I kept running into problems I wanted to solve myself, and I found that iterating directly with AI often got me much closer to the system I actually had in my head than trying to explain every little requirement to someone else. 🧠 The architecture The difficult part was that I was not starting from scratch. I had multiple existing knowledge stores to reconcile, and I wanted the same ICM to serve two completely different agent harnesses: Hermes Agent, running on a VPS so I can access it from anywhere, and Claude Code, running locally on my Mac. But Hermes itself was not just one simple agent session. On the VPS, the same Hermes engine runs three persistent profiles: my main Hermes environment, a separate staff profile connected to my assistants through Slack, and an ICM pilot profile. On top of those, Hermes can dynamically spin up multiple temporary sub-agents for parallel work. Those environments can reach Hermes through several independent paths: my Mission Control interface, Slack, mobile/web access, n8n automations, and a live two-way bridge between Claude Code on my Mac and Hermes on the VPS. So the real problem became concurrency. I effectively had multiple Hermes personas, execution paths, bridges, users, automations, and temporary sub-agents all capable of interacting with the same underlying memory architecture.