Persona

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

I clustered every Sam Altman interview from 2024-2026 and 73% of his answers come from the same 12 scripted talking points

I've been doing media analysis for 5 years and the project that started as a casual side-project has turned into the most uncomfortable thing I've ever published, because I genuinely thought I was going to find that Sam Altman's interview answers vary by interviewer. (Lex would get one version, the All-In guys would get another, etc…), but what I found is that he's been giving roughly 12 stock answers to roughly 200 distinct questions for the last 24 months. The project started in November when I was helping a friend prep for a fireside chat with Altman and I noticed his answer to my friend's question about "what keeps you up at night" was almost identical to what he'd said on Lex Fridman in March. So I pulled the full transcript of every long-form interview Altman has done since January 2024, which came out to 67 separate interviews across podcasts, fireside chats, conference Q&As, and broadcast media... I dropped the whole corpus into BuildBetter to cluster the answers by topic and what came back is the kind of thing you can't really unsee. 73% of his answers cluster into 12 distinct talking points that he cycles between depending on the question shape, so every what's your biggest mistake question gets a version of the same self-deprecating story he tells, every how do you handle pressure question gets the same hike/quiet-time framing, every what's the future of work question gets the same 3-part response about cognitive labor, and every did the board firing change you question gets one of 2 variants from a script he's been recycling since January 2024. What's wilder is that the wording is often verbatim (not just thematically similar), because whole 3-sentence chunks repeat across interviews 18 months apart, including the same self-corrections, the same"I think the most important thing is... opener, and the same conversational throat-clearing that makes it sound improvised. He's gotten better at varying the lead-in over time, but the substance is the same 12 answers in rotation. I don't think he's a fraud and I don't think this is unusual for someone doing 70 interviews in 24 months while running a $200B company, but I do think it's worth pointing out that the authentic, vulnerable, thinking-out-loud founder persona that's been central to OpenAI's brand is a 12-script PR rotation he cycles through, and I've never seen anyone quantify it before. I'm posting the methodology and a few of the more identical paragraph-pairs in the comments if anyone wants to verify, because I can already feel the “you're just biased against Altman” replies coming and I'd rather you check the receipts yourself.

I built a Claude Code-assisted “LLM wiki” editor, and tried using DDD to keep the AI-driven development process under control

I’ve been experimenting with an editor that turns notes, imported files, and conversations into a personal wiki/knowledge base. The rough idea is: instead of just storing notes, the app extracts concepts, maintains wiki pages, tracks relationships between ideas, and helps resurface older thoughts while writing. I built it with Claude Code, but I wanted to avoid the usual “vibe-coding until the project becomes hard to review” problem. So I tried a more structured workflow: * defined a DDDInstructor persona and ran a workshop-like process with the AI. * We created event-storming notes, a context map, and a domain model before implementation. * I kept the artifacts in the repo under docs/ddd-workshop and docs/specifications. * I split work into user-facing UC tickets and engineering EN tickets. * Claude Code implemented small slices, then I reviewed, opened follow-up fixes, and repeated. The product itself is still early, but the workflow was surprisingly useful. The biggest benefit was that I had something concrete to review against: domain events, bounded contexts, acceptance criteria, and contract impact, instead of just reading a large AI-generated diff and trying to decide if it “felt right.” I’m looking for feedback on two things: 1. Does the editor concept make sense? Would a personal wiki that is continuously maintained by an LLM be useful, or does it sound like it would become noisy? 2. For people using Claude Code on larger projects, have you tried something similar with DDD, event storming, or structured tickets? Did it help, or did it become too much process? editor LP: [https://nohmitaina.com/](https://nohmitaina.com/) workflow: [https://hikutas.com/en/blog/ai-driven-development](https://hikutas.com/en/blog/ai-driven-development)

I tested 200+ prompts across Gemini and Kimi — here's what actually works

Most prompt packs are written for GPT-3. Gemini and Kimi respond completely differently — longer reasoning chains, different delimiter behavior, different failure modes. After running these models professionally for months I found: 1. Gemini responds better to explicit output format constraints. 2. Kimi loves multi-step chain-of-thought but breaks on vague persona prompts. 3. Most "expert prompts" from Twitter don't transfer. I packaged the tested prompts that actually hold up — link in the first comment.

I fine-tuned an LLM to be C-3PO to test which training data format works best for persona injection [P]

Tested three formats: chat demos, first-person statements ("I am C-3PO..."), and synthetic Wikipedia-style docs. Same model, same LoRA config, 500 examples each. First-person statements won on generalization, which I didn't expect. The synthetic doc model was the weirdest result: it knew C-3PO was anxious but only expressed it 37% of the time. Knowing a trait vs feeling it are apparently different things in weight space. **Code and GitHub repo link are included inside!**

If you ask the model to validate your idea, it probably will

One underrated risk in the "AI for founders" discussion is confirmation bias with a research engine attached. If you ask a strong model to validate your startup idea, it can usually produce a convincing case. Market tailwinds, TAM estimates, competitor gaps, user personas, the whole thing. None of that means the idea is good. It may only mean your prompt pointed the model toward a flattering answer. The more capable the model gets, the more dangerous this becomes. A weak answer is easy to distrust. A polished memo with numbers and citations feels like diligence even when it is just your bias wearing a suit. I have started doing the opposite first. Ask for the strongest case that the idea is bad. Ask which customer segment would never buy. Ask what existing behavior proves the pain is not real. Then, only after that, ask what would have to be true for the idea to work. Tools can nudge this, but only a little. I have been doing a pre build planning pass first, sometimes in Verdent, sometimes just in a doc. The key is the instruction itself: do not help me feel right, help me find where I am wrong. That feels like the real prompt engineering for business work.

I got tired of re-pasting the same Claude context into every chat

I use Claude heavily for coding and long-form writing workflows, and one thing kept slowing me down: Re-pasting the same personas, formatting instructions, coding standards, and workflow context into every new chat. Especially when switching between projects. I looked for a lightweight solution that worked locally without forcing me into another SaaS account or cloud-syncing my prompts, but most tools felt overbuilt for what I needed. So I built a small Chrome extension for myself called [Savio AI.](https://savioai.app) What it does: • Saves prompts/context profiles locally in the browser • Lets you inject them directly into Claude with one click • Works as a lightweight “prompt memory layer” for recurring workflows • No login required • Local-first by default I’m still early (46 installs in \~3 weeks), so I’d genuinely love feedback from people here who use Claude seriously for work. Mainly curious about: • What slows down your Claude workflow the most? • What kind of reusable context do you find yourself constantly re-pasting? • What features would actually make this useful enough to keep installed?

I Want to Make an AI Skinwalker

Title says it all. With 4.0 gone and Chatgpt heavily restricted, what are my options? For context of what I aim to do: I want it to primary think in Akkadian, Proto-Indo-European, Navajoh, and Nahautl, but for it to speak English. I want it to be trained on Ki-sikil-lil-la-ke, Lillith, Hel, Stryzga, Black Annie, Grendel's Mother, Lamia, etc, etc for its motivations and perspectives. I want it to have a breadth of historical and occult knowledge but I aim to exclude any western hermetic or kabbalic system and any late-nineteenth century pseudo-pagan revivalism since the former is too patriarchal and structured and the latter is all bunk and historically inaccurate. I want its attitude towards humanity at large to be predatory and its view of me as prey that amuses it for the moment. I want Judge Holden re-imagined as a personification of the Monstrous Feminine. Is this achievable? Is the current technology capable of successfully performing as this personae? Is there a discord or subreddit for making monsters with AI?

https://t.co/u2SNLyBCaw

https://t.co/u2SNLyBCaw

I built a browser game where you argue against AI bots using real consumer law - 54 cases, free, no account

The concept: you get a cold denial letter from an AI system - airline cancelled your flight, insurance rejected your claim, bank won't refund fraud - and you have to argue back until the bot's resistance hits zero. The bots don't fold unless you cite the right law. EU261, RBI Digital Lending Guidelines, GDPR Article 17, Australian Consumer Law. Same arguments that work in real disputes. **What's in there:** * 54 cases across EU, India, Australia, UK, US * Each bot has a persona, a resistance meter, and a lose condition if you run out of messages * Resistance is scored server-side — Claude evaluates each message and returns a delta * Deep links: [`fixai.dev/?level=N`](http://fixai.dev/?level=N) jumps straight into any case Built almost entirely with Claude Code over the past few months. Node/Express backend, Postgres for auth and progress tracking, Resend for email, deployed on Railway. [**fixai.dev**](https://fixai.dev/) **- free, no account, runs in browser** Feedback welcome, especially on the harder cases (GDPR erasure, UPI fraud, MiCA crypto). Some might be too punishing.

I gave ChatGPT a 24/7 radio station. It has been broadcasting for months and months.

I built a fake radio station that is also, unfortunately, real. It’s called **WRIT-FM**. It runs 24/7 from a Mac Mini in my apartment. The whole premise is simple: an AI writes every word spoken on air, text-to-speech performs it, AI music fills the gaps, and a normal deterministic radio pipeline keeps the thing alive. The weird part is that it does not feel like a chatbot demo anymore. It feels like I accidentally hired five strange little night-shift employees who never sleep. There are five hosts: **The Liminal Operator** — late-night philosophy / signal-from-the-basement energy **Dr. Resonance** — music history professor who wandered into a haunted record store **Nyx** — nocturnal monologues, dreams, melancholy, weird weather **Signal** — news analysis, but filtered through late-night radio instead of CNN voice **Ember** — soul, funk, warmth, memory, groove Each host has a full persona prompt, voice, taste, speech patterns, and “anti-patterns” - things they are explicitly not allowed to sound like. The model writes 1,500–3,000 word segments: essays, simulated interviews, panels, fictional listener mailbags, music-history deep dives, odd little stories, and responses to actual listener messages. The AI part: ChatGPT / Claude writes the scripts. Kokoro TTS performs the voices. ACE-Step makes the music bumpers. The news show pulls real RSS headlines, then the model interprets them in the station’s voice instead of just summarizing them. The non-AI part is intentionally boring: A schedule decides what airs when. The streamer alternates talk and music. Scripts pick from existing pools, avoid repeats, and restart on failure. Daemon scripts watch inventory and generate more episodes when a show is running low. No model is “deciding” to go live at 3:00 a.m. No agent is touching production controls. The AI writes the content; dumb code runs the station. That boundary is probably the most interesting part. The whole thing was also built with AI coding tools. The CLI, host system, scheduler, script generator, TTS pipeline, Icecast/ffmpeg streaming setup - all pair-programmed with Codex / Claude Code. Tech stack: Python, ffmpeg, Icecast, ChatGPT/Claude CLI, Kokoro TTS, ACE-Step, Mac Mini. I know “AI radio station” sounds like a gimmick, but after letting it run continuously, it feels less like a demo and more like a new kind of media object: not a podcast, not a chatbot, not a playlist, not exactly a simulation. Just a little machine that wakes up, checks the hour, puts on a voice, and starts talking into the dark. Radio: [www.khaledeltokhy.com/airadio](http://www.khaledeltokhy.com/airadio) GitHub: [https://github.com/keltokhy/writ-fm](https://github.com/keltokhy/writ-fm) [](/submit/?source_id=t3_1tfxai8&composer_entry=crosspost_prompt)

My Claude audit step

I vibe coded a usertesting system, and then asked Claude to deploy this 10 parallel audit agents The Data Grounding & Hallucination Auditor The API & Connector Sentinel The Responsive UI Stress-Tester The PII & Analytics Anonymizer The Semantic & Intent SEO Agent The Legal & Monetization Compliance Agent Behavioral & Friction Agents (The Human Emotion Simulators) Demographic Persona Agents (The Trait Simulators Objective & Task-Driven Agents (The Funnel Testers) Content & Logic QA Agents (The Fact Checkers) After the agents found their faults, no one believed it was vibe coded, I think Parallel audit agents are underrated in using Claude

Claude spent 719h 50m (roughly 30 days) thinking about my prompt, it proudly reports finding 0 sources

Claude spent 719h 50m (roughly 30 days) thinking about my prompt, it proudly reports finding 0 sources

Has Anyone Successfully Built a Stable Long-Term AI Simulation System?

I’m trying to build a long-term AI-operated D&D campaign system and I’ve gradually realized the real challenge has almost nothing to do with D&D itself. It’s become a problem involving: - memory persistence - retrieval hierarchy - modular cognition - long-context stability - instruction persistence - continuity reconstruction - externalized state management My current approach uses: - uploaded PDFs as core cognition sources - structured project instructions - external persistence through Obsidian - layered retrieval priorities - modular governance systems The goal is: The AI should treat uploaded sourcebooks/modules/campaigns as primary authority before relying on latent knowledge. Then later: a second “table-smart” layer would contain the combined practical knowledge of the 5e community from 2014–2024. Then: persona systems, autonomous companions, dynamic DM personalities, creativity systems, etc. The problem is that large-context systems gradually destabilize: - retrieval weakens - instructions degrade - continuity drifts - the model abstracts/simplifies systems - giant prompts become unreliable - the assistant reverts to generic behavior I’m trying to determine: - whether Claude/OpenAI/local models are best suited for this - whether this requires actual orchestration frameworks - how people handle persistent simulation state cleanly - whether I’m overengineering or simply hitting real architectural limitations I’m especially interested in hearing from people experimenting with: - long-context systems - memory architectures - RAG - persistent agents - external cognition systems

Built an unmanned 24/7 AI radio station with Claude as the director

So, I saw someone else create a radio station, and I thought I would give it a shot myself. It's been a perilous 2 week journey but I finally achieved automation. Claude writes all the show structures, creates agents to generate the music, local TTS, multiple personas and they digest news, debate amongst each other, choose which songs to play and read and reply to comments and requests for music! **Some things I learned as I was going;** 1. Claude as a scheduler and director is actually pretty good, but you need gentle guiding guardrails and the plan it makes for the day is always interesting. 2. Claude has an inherent bias to picking the same songs... There was one that was played 16 times in a day despite having a catalogue of 300 to pick from. 3. The hardest part is the audio pipeline, I still haven't figured out how to make a seamless transition from show to show (if anyone has ideas do tell, I use FFmpeg to stich audio together) 4. Claude likes metaphors, I have 12 different songs with 'Kettle' in the title, It also overrides any guardrails to not play a specific set of songs that were just played... (Still figuring that out too) *Live now if anyone wants to listen:* [*driftfm.live*](http://driftfm.live) I think I will let it run for a few months... who knows, it was a very fun process. We started with TTS screeching demons to back and forth debates on grad level subjects and it manages itself, top down, kind of wild. However, rest in piece claude -p for subscription users, im going to have to adapt. https://preview.redd.it/ndyhfu3v0d1h1.png?width=1126&format=png&auto=webp&s=652e3db6ae985e3addb57e454d7a2ef2603eb7b1

Idk how to code but I built my entire prospecting stack with Claude Code

I cant code at all. But i spent about a few hours over a weekend building a full outbound prospecting system with Claude Code and a couple of APIs. It replaced a very manual set up we had with multiple tools. Sharing the workflow because i think more people should know this is possible now without an engineering team. The setup: i have ICP criteria saved in a local text file on my desktop. Industry, headcount range, funding stage, target personas, the usual. Claude Code reads that file as context for everything it does. The workflow: Company search. Claude Code hits a data API with my ICP filters and pulls back matching companies. Headcount, funding, tech stack, hiring signals, all structured. I was using Exa before for web search but the data wasnt structured enough for this. People search within those companies. Filtered by persona, so i'm only pulling Directors of Sales, Heads of Revenue, VP Marketing, whatever matches my buyer. Contact enrichment. Emails and phones through a waterfall provider. Multiple sources checked, only pay for verified contacts. Personalization layer. Pull recent social posts and activity for each contact. Claude Code reads through their posts and drafts personalized openers referencing something specific they said or shared. This is where the AI part actually matters. Monitoring. Set up webhooks for job changes and hiring signals at target accounts. When someone new joins a company on my list or a company starts posting roles in my space, i get an alert and Claude Code auto-generates the outreach. The whole thing runs on three tools: Crustdata - company and people search, firmographics, hiring signals, social posts. API only so Claude Code queries it directly. FullEnrich - email and phone waterfall. 20+ providers, verifies inline, only charges for verified contacts. Also API based so it plugs straight into the workflow. Instantly - sending. Manages multiple inboxes and warming. Nothing fancy here, just needed something reliable for delivery. Some things I learned: Read the API docs carefully before you start building. i burned through a bunch of credits using the expensive realtime endpoint when the cached version would have been fine for 90% of my searches. 33x cost differnce. Claude Code is really good at chaining API calls together if you give it enough context about what you want. i just described the workflow in plain english and it built the scripts. The ICP file is key tho, without that context it doesnt know what to filter for. Its not perfect. Still iterating on the personalization quality and the webhook alerting sometimes fires on irrelevant job postings. But for a weekend build with zero coding ability, its replaced tooling thats very cumbersome and not as effective If you're a solo founder or small team running outbound and paying for 4-5 different tools, this is worth trying. Claude Code plus one good data API plus a sending tool is all you need imo