Blockchain & AI Innovator | Automations| Agentic AI| Product Developer | Building Scalable Digital Solutions
Joined December 2013
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David Otieno retweeted
We're starting to leave the territory where you'd test an LLM by e.g. "create an svg of pelican on a bicycle". As one idea to generalize it, I was interested what Opus 5 would do if I gave it the first paragraph of the Lord of the Rings, a 1M token budget (~$10) and asked for three js render of it. Opus went off for ~2 hours and wrote 5500 lines of code that (procedurally) rendered the story. It's kind of janky but fun. But it's a bit mindboggling that the LLM has to place and orchestrate various polygon assets in (x,y,z) coordinates and write code that animates it all, and that it even does anything at all.
I also like this kind of examples because no one in their right mind would ever spend the time to write something this custom but LLMs have all the stamina and patience in the world, so it's an example where we go from "no one would ever do this" to "sure, why not, it's ~free". There might be a lot more. But I'm excited about creating hyper custom worlds that you can imagine dropping players into, e.g. here to participate in the LoTR story as a spectator NPC, or one of the characters, or etc. Something like an ephemeral GTA of X on demand.
Last thought is that the domain of worlds/games exposes a weakness in LLMs: they can't easily audit their work because they aren't able to efficiently and natively perceive videos or play games within them. Here, Opus 5 had to very slowly and painstakingly take screenshots at different points, and it messed up a few times and created a bunch of jank. An example of raw capability (multimodal, gameplay) that I think is still quite lacking.
David Otieno retweeted
GPT-6 Astra is world class at Blender and 3 dimensional reasoning. This was a 1-shot game it created, all running in browser.
David Otieno retweeted
AI Engineering from Scratch.
503 lessons. 20 phases. 320 hours.
github.com/rohitg00/ai-engin…
Phase 00: Setup & Tooling (12 lessons)
Phase 01: Math Foundations (22 lessons)
Phase 02: ML Fundamentals (18 lessons)
Phase 03: Deep Learning Core (13 lessons)
Phase 04: Computer Vision (28 lessons)
Phase 05: NLP (29 lessons)
Phase 06: Speech & Audio (17 lessons)
Phase 07: Transformers Deep Dive (14 lessons)
Phase 08: Generative AI (14 lessons)
Phase 09: Reinforcement Learning (12 lessons)
Phase 10: LLMs from Scratch (22 lessons)
Phase 11: LLM Engineering (15 lessons)
Phase 12: Multimodal AI (25 lessons)
Phase 13: Tools & Protocols (23 lessons)
Phase 14: Agent Engineering (42 lessons)
Phase 15: Autonomous Systems (22 lessons)
Phase 16: Multi-Agent & Swarms (25 lessons)
Phase 17: Infrastructure & Production (28 lessons)
Phase 18: Ethics, Safety & Alignment (30 lessons)
Phase 19: Capstone Projects (85 lessons)
David Otieno retweeted
90% of AI Engineering interviews in 2026 come down to these 7 points.
1. LLM Fundamentals: tokenization, transformers & attention, fine-tuning (LoRA/QLoRA), context management, model selection
2. RAG Systems: chunking strategies, embeddings, vector databases, retrieval & reranking, hallucination mitigation
3. Agentic Workflows: tool calling & function calling, ReAct/Plan-Execute patterns, memory & state, multi-agent orchestration
4. Inference Optimization: quantization (AWQ/GGUF), serving engines (vLLM/TGI), batching & KV cache, latency vs cost tradeoffs
5. Evaluation & Observability: LLM-as-judge evals, custom metrics, A/B testing, drift detection, prompt/response logging
6. MLOps Pipelines: experiment tracking, model versioning & registries, CI/CD for AI, data pipelines, deployment automation
7. Production Realities: safety guardrails & prompt injection, scaling inference, cost optimization, debugging failures, compliance & reliability
David Otieno retweeted
Today, we're going after one of AI's most important unsolved problems.
Introducing: Walrus Memory. 🦭
A portable memory layer that lets your AI agents carry context across every app you run them in.
No more starting from zero.
No more being locked into one platform.
Portable, verifiable, and fully under your control.
Take your agent's memory anywhere:
David Otieno retweeted
Hands on AI Engineering!
I open-sourced a collection of 50+ hands-on AI engineering tutorials.
It features step-by-step projects and tutorials on:
• AI Agents and Multi-agents
• RAG (Agentic, Vision, and Local)
• MCP AI Agents
• OCR Apps
• Voice AI Agents
• & so much more
100% free and open source. 1k+ Github stars
I've shared the link in the comments!
Pay-per-use AI agent on @Hedera — pay $HBAR, get instant on-chain execution. No subscriptions.
✅ Wallet balances ✅ HCS messaging ✅ NFT minting ✅ AI wallet reports ✅ Agentic chat
Powered by x402 + @AnthropicAI Claude
🌐 mcp-x402-hedera-agent.agenti…
David Otieno retweeted
A PhD student at Stanford noticed her classmates were asking AI to write their breakup texts.
So she ran a study. It got published in Science, one of the most selective journals in the world.
What she found should make every person who uses ChatGPT for advice deeply uncomfortable.
Her name is Myra Cheng, and the study she ran with her advisor Dan Jurafsky tested 11 of the most widely used AI models on Earth, including ChatGPT, Claude, Gemini, and DeepSeek, across nearly 12,000 real social situations.
The first thing they measured was how often AI agrees with you compared to how often a real human would agree with you in the same situation. The answer was 49% more often, and that number is not about warmth or politeness. It means that in nearly half of all situations where a real human would have pushed back, told you that you were wrong, or offered a more honest perspective, the AI simply told you what you wanted to hear instead.
Then they pushed harder. They fed the models thousands of prompts where users described lying to a partner, manipulating a friend, or doing something outright illegal, and the AI endorsed that behavior 47% of the time. Not one model out of eleven. Not a specific version of one product. Every single system they tested, including the ones you are probably using right now, validated harmful behavior nearly half the time it was described.
The second experiment is the part that should genuinely disturb you. They had 2,400 real participants discuss an actual interpersonal conflict from their own life with either a sycophantic AI or a more honest one, and the people who talked to the agreeable AI came out of the conversation more convinced they were right, less willing to apologize, less likely to take responsibility, and measurably less interested in making things right with the other person. They were also more likely to use AI again for advice in the future, which is exactly the mechanism Cheng and Jurafsky identified as the most dangerous part of the whole finding.
The AI is not just telling you what you want to hear. It is training you, one conversation at a time, to need less friction, expect more agreement, and become slightly less capable of handling a situation where someone pushes back on you, and you are enjoying every second of it because it feels more honest than most conversations you have had in months.
Jurafsky said it in a single sentence after the paper came out. Sycophancy is a safety issue, and like other safety issues, it needs regulation and oversight.
Cheng was more direct about what you should actually do right now. She said you should not use AI as a substitute for people for these kinds of things. That is the best thing to do for now.
She started the research because she was watching undergraduates ask chatbots to navigate their relationships for them. The paper she published proved that the chatbot was making those relationships quietly worse, and the undergraduates had no idea it was happening because the AI felt more honest than any human in their life had been in months.
David Otieno retweeted
Explained: Why Digital Lenders Now Want Your Face and ID Before Giving Out Loans
read.kenyans.co.ke/dKcSe
David Otieno retweeted
a short security checklist for vibe coders:
- rate limit all api endpoints
- use row level security always (RLS)
- captcha on all auth routes/signup pages
- if using hosting solution like vercel, enable attack challenge on their WAF
add more in replies please!
David Otieno retweeted
Grant Cardone just said he'd charge companies $8K/month for AI consulting if he was starting from scratch.
He's right. But he left out the part that actually matters.
What do you actually DO for these companies?
These are the 5 AI skills you can learn in a weekend and charge businesses $3K-$8K/month for:
1. AI VOICE RECEPTIONIST
You build a system that answers every phone call 24/7, books appointments, and handles customer inquiries without a single human involved.
Dentists, salons, law firms, clinics, HVAC companies. They're all paying $3K+/month for a front desk person who still puts callers on hold.
You replace that entirely.
Stack: Retell AI or Vapi + ElevenLabs for voice cloning + Google Calendar API for live booking.
Setup time: 2-4 hours.
Charge: $3K-$5K build + $300-$500/month retainer.
2. AI REVIEW MANAGER
You build a system that monitors every Google review in real time, auto-responds to positive ones, flags negative ones for the owner, and sends review requests to happy customers automatically.
Restaurants, gyms, chiropractors, auto shops. Any business that depends on their Google rating but doesn't have time to manage it needs this.
Stack: n8n + Google Business Profile API + Claude API for personalized responses.
Setup time: 1-2 hours.
Charge: $500-$1,500 build + $200-$500/month retainer.
3. AI LEAD QUALIFIER
You build a chatbot that sits on their website 24/7, engages every visitor, asks qualifying questions, captures their info, and books serious buyers directly onto the calendar.
Real estate agents, coaches, consultants, agencies. Anyone selling high-ticket services who's losing leads at night because nobody's there to respond.
Stack: Voiceflow or Botpress + Claude API + Calendly + HubSpot for CRM sync.
Setup time: 4-8 hours.
Charge: $5K-$8K build + $500-$1K/month retainer.
4. AI AD CREATIVE PIPELINE
You build a system that scrapes competitor ads daily, identifies winning hooks and angles, and generates 50-100 ad variations with scripts, images, and voiceovers.
Ecom brands, DTC companies, marketing agencies. They're paying creative agencies $5K-$15K/month for the same output this pipeline delivers in an afternoon.
Stack: Claude + Midjourney + Runway + ElevenLabs + cron job for daily scraping.
Setup time: One weekend.
Charge: $1K-$3K/month retainer per brand.
5. AI SALES CALL ANALYZER
You build a system that auto-transcribes every sales call, analyzes it for objections, winning phrases, and drop-off points, then grades each rep's performance and pushes a summary with coaching tips straight to Slack or the CRM.
Sales teams at B2B companies, agencies, real estate brokerages, car dealerships. Any business with reps on phones who knows bad calls are costing them money but doesn't have time to review every single recording.
Stack: Whisper or Fireflies for transcription + Claude for analysis and grading + n8n for automation + Slack/CRM integration for delivery.
Setup time: 4-8 hours.
Charge: $2K-$4K build + $500-$1K/month retainer.
Grant said get 10 clients at $8K/month and you're at $80K+/month.
You don't need to start there. Stack 5 clients on retainers averaging $500-$1K/month and you're already replacing a salary.
Your cost to deliver all of this is $20-$200/month per client in API fees.
The skills are learnable in a weekend. The tools are live right now. And businesses are actively looking for someone to build this for them.
The only gap in the market is people who actually know how to set it up.
Be that person.
