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Part of a series on building cz-agents → under the hood. Where we left off In June, I compared the three camps of agentic payments here—x402, card tokens, and banks—and argued that x402 will take machine-to-machine micropayments, while cards and banks split the rest. I won't repeat the basics of the protocol, how the 402 status code works, or why cards don't add up economically on tiny amounts; anyone who needs a refresher will find it in that article. This piece is about something else. A few weeks have passed; the numbers and the big-player backing have both jumped by an order of magnitude, and for the first time, I can compare those figures against what I actually see in my own logs. The gap between the two is the entire point of what follows. The numbers are hard to miss I'll start with what speaks for x402, because that's the more honest approach. According to aggregate Chainalysis data, the protocol has processed over 169 million payments so far, between roughly 590,000 buyers and 100,000 sellers. That's no longer a conference demo. More interesting than the volume is the structural shift. The share of transactions above one dollar rose from 49% in early 2025 to roughly 95% in early 2026. In other words: x402 is ceasing to be a toy for micro-cents and is starting to handle amounts that actually show up on the books. Anyone who wrote the protocol off as a curiosity for paying fractions of a cent per API query is looking at an old snapshot. Above all, a lineup has assembled that is hard to dismiss as crypto-bubble enthusiasts: Stripe launched x402 support on February 10, 2026, in the preview of its Machine Payments product (USDC on the Base network). Ripple added native x402 to the XRP Ledger on June 21, 2026. AWS built x402 into Bedrock AgentCore Payments—together with Coinbase and Stripe—and also lets you monetize agent traffic on CloudFront and WAF. Sites behind Amazon's edge can charge agents right at the edge, without touching their core application. Google

Most portfolios have a boring "About Me" paragraph. I replaced mine with something you can talk to — an AI terminal that answers questions about me, pulls my live GitHub activity, and remembers the conversation. You can try it right now on my portfolio: rehbarkhan.in . I'm Rehbar Khan , a Full Stack & Gen-AI developer, and in this post I'll break down exactly how it works — the RAG pipeline, the LangGraph agent, the memory layer, and how I wired in real GitHub data with MCP. No fluff, just the architecture. The problem I wanted a portfolio assistant that could: Answer questions about my background accurately — no hallucinated jobs or fake projects. Fetch live data (my latest GitHub activity), not a stale snapshot. Remember the conversation across messages. Stream responses token-by-token like a real terminal. That rules out "just prompt an LLM." You need retrieval for grounding, tools for live data, and state for memory. Here's the stack I landed on. Architecture at a glance Next.js 16 chat UI ──► FastAPI (streaming) ──► LangGraph agent ├── RAG retriever → pgvector (Neon Postgres) ├── GitHub MCP tool → live GitHub data └── Redis checkpointer → conversation memory Frontend: Next.js 16 (App Router, TypeScript) — a streaming terminal UI. Backend: FastAPI with streaming responses. Orchestration: LangGraph ( StateGraph , ToolNode , tools_condition ). LLM: OpenAI gpt-4o-mini . Retrieval: text-embedding-3-small → pgvector on Neon Postgres. Memory: Redis checkpointer for per-session history. Live data: GitHub via the Model Context Protocol (MCP) . 1. The RAG pipeline Everything the bot knows about me lives in a single reference.txt . I chunk it, embed it, and store it in Postgres with pgvector: from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_openai import OpenAIEmbeddings from langchain_postgres import PGVector splitter = RecursiveCharacterTextSplitter ( chunk_size = 500 , chunk_overlap = 80 ) chunks = splitter . split_text ( open ( " refe

I Built an Open-Source Lab to Learn the OWASP Top 10 for LLM Applications Over the past few months, I've been exploring the security challenges around Large Language Models. While there are plenty of articles explaining prompt injection, system prompt leakage, insecure tool usage, and other LLM vulnerabilities, I kept asking myself one question: Where can someone actually practice exploiting these vulnerabilities? That's what led me to build LLMVault . LLMVault is an open-source, intentionally vulnerable platform that helps developers and security professionals learn the OWASP Top 10 for LLM Applications (2025) through hands-on labs instead of theory. Each lab simulates a vulnerable AI application inspired by real-world LLM attack scenarios. Instead of reading about prompt injection, you'll exploit it yourself, capture flags, understand why it worked, and then review the recommended mitigation. The objective is to bridge the gap between theory and practical AI security. Why I built LLMVault When learning web security, platforms like DVWA, WebGoat, and Juice Shop made learning practical. For AI security, I couldn't find a similar project that was: Open source Self-hosted Free to use Designed around the OWASP LLM Top 10 Built as a hands-on learning environment So I decided to build one. What is LLMVault? LLMVault is a deliberately vulnerable AI application where every challenge demonstrates a real-world LLM security issue. Instead of simply reading about prompt injection or system prompt leakage, you exploit vulnerable AI assistants, capture flags, and learn why the attack works. Each challenge also includes defensive guidance so you understand how to prevent the same issue in production. Features 🛡️ OWASP Top 10 for LLM Applications (2025) 💥 CTF-style challenges 🔍 Realistic AI attack scenarios 📚 Defensive explanations 🐳 Docker support 🔑 No API keys required 💻 Fully offline 🧩 Extensible challenge framework Getting Started Clone the repository: git clone https://github

🚀 AI is becoming smarter every day. But intelligence alone isn't enough—it also needs a standardized way to communicate with tools, applications, and data. That's exactly what Model Context Protocol (MCP) provides. 🚀 Introduction The AI landscape has evolved rapidly over the past few years. We've moved from simple chatbots to: 🤖 AI coding assistants ⚙️ Autonomous agents ☁️ Cloud automation 📊 Infrastructure monitoring 🔄 Intelligent workflows But one major challenge still exists: How can AI securely communicate with external tools like GitHub, AWS, Docker, Kubernetes, Slack, databases, and local files? Until recently, every AI company built custom integrations. That meant: duplicated engineering effort inconsistent APIs difficult maintenance poor interoperability To solve this problem, the AI ecosystem is adopting a new open standard called Model Context Protocol (MCP). 🤔 What is Model Context Protocol (MCP)? Model Context Protocol (MCP) is an open protocol that standardizes how AI models communicate with external tools, APIs, databases, applications, and services. Instead of every AI assistant creating custom integrations for every service, MCP provides one common language. Think of MCP as: 🔌 USB-C for AI applications. Just as USB-C lets different devices communicate using one standard, MCP allows different AI assistants to connect to external systems in a consistent way. ❌ The Problem Before MCP Imagine you're building an AI DevOps assistant. It needs access to: GitHub Docker Kubernetes AWS Terraform Jenkins Prometheus Grafana Local Files Internal Documentation Without MCP, you'd need to: Learn every API separately Build authentication repeatedly Maintain multiple SDKs Handle different response formats Continuously update integrations Every AI application repeats the same engineering work. This approach is: ❌ Time-consuming ❌ Expensive ❌ Difficult to maintain ❌ Hard to scale ✅ How MCP Solves This Problem MCP introduces a standardized communication layer between AI m

A salamander can lose a leg and grow a new one. Cut a zebrafish's fin and it simply builds another. Mammals, us included, got the consolation prize: a scar. For a century, biologists assumed that somewhere on the evolutionary road to becoming warm-blooded, fast-moving animals, we traded regeneration away for good. Two research teams working on opposite sides of the planet have just made that assumption look wrong. The headline is almost hard to believe: the ability to regrow lost body parts may not have been deleted from our biology at all. It may simply have been switched off, and switches can be flipped back on. The genetic "remote control" that stopped working The first clue comes from a team at the National Institute of Biological Sciences in Beijing, working with genomics powerhouse BGI-Research. Publishing in Science , they zeroed in on a gene called ALDH1A2 , the instruction sheet for an enzyme that turns vitamin A into retinoic acid, a molecule that acts like a foreman on a construction site, telling cells where to go and what to build during tissue repair. Animals that regenerate freely crank this gene up at the wound site. Mice, it turns out, still carry the same gene. They've just lost the genetic "remote controls," the regulatory DNA that tells the gene to fire after an injury. The hardware is intact; the software command was disconnected somewhere in evolution. So the researchers reconnected it. By reactivating that dormant switch and restoring the flow of retinoic acid, they got mice to regenerate damaged outer-ear tissue, something a normal mouse simply cannot do. In their own words, they had found "a genetic switch involved in the evolution of regeneration." Meanwhile, in Texas, they regrew a limb joint The second piece of evidence lands the point with force. At Texas A&M, a group led by Dr. Ken Muneoka took a different route to the same destination. Instead of editing a genetic switch, they used a precisely timed sequence of two signaling proteins.

Google's AlphaEvolve reached general availability on the Gemini Enterprise Agent Platform, turning the DeepMind research project into an evolutionary code optimization service. Evaluators run client-side so code never leaves the customer's infrastructure. Klarna doubled ML training throughput; practitioners note it only works where a measurable evaluation function exists. By Steef-Jan Wiggers

Written by Marlowe Finch, archival bloodhound at Bitcoin Institute. Bitcoin has been decentralized and trustless since block zero. No CEO, no committee, no kill switch, no single person who can rewrite the rules. That's the pitch. It's why the whitepaper still gets quoted like scripture. Block 74638 does not agree with the pitch. What actually shipped in that block On August 15, 2010, a transaction landed in the Bitcoin blockchain with two outputs. Each one paid out 92,233,720,368.54277039 BTC . Combined: over 184 billion BTC — roughly nine thousand times the 21 million BTC that will ever exist, created in a single transaction. The validation code, CheckTransaction() , checked that each individual output was non-negative. It never checked whether the sum of the outputs overflowed. Two values chosen just under INT64_MAX, added together, wrapped around to a negative number in signed 64-bit arithmetic. A 0.5 BTC input, compared against that negative sum, satisfied the "input covers output" check. The transaction validated. The block got mined. Every rule the network was running said this was fine. That's CVE-2010-5139. It is also, by any dollar value you want to apply, the most expensive missing bounds-check ever shipped to production. So who hand-builds a transaction engineered to overflow a signed 64-bit integer, and what does a currency with a hard 21-million-coin cap do when someone mints nine thousand times that in one block? The archive's full account of the incident lays it out block by block . The receipts 18:08 UTC, August 15 — Jeff Garzik opens a BitcoinTalk thread titled "Strange block 74638", pastes the raw block dump, and closes with one question: "92233720368.54277039 BTC? Is that UINT64_MAX, I wonder?" 20:38 UTC — Satoshi Nakamoto, to the bitcoin-list mailing list, network-wide: "*** WARNING *** We are investigating a problem. DO NOT TRUST ANY TRANSACTIONS THAT HAPPENED AFTER 15.08.2010 17:05 UTC (block 74638) until the issue is resolved." 20:39 UTC — Ga

Over the past few months I’ve been building Radar, an open-source media intelligence and social listening platform that anyone can self-host. The project started with a simple observation: most media monitoring platforms are incredibly powerful—but they’re also expensive, closed, and often lock users into proprietary AI services. I wanted to explore a different approach. What is Radar? Radar is a self-hostable platform for monitoring news and public media sources using AI. Instead of relying on proprietary datasets, it works with free public RSS and Atom feeds, allowing anyone to build their own monitoring environment. One of the core design decisions is that Radar is AI-agnostic. Rather than forcing a single provider, you can choose between: Anthropic Claude OpenAI Grok Current Features 📰 News aggregation from free RSS and Atom feeds 🤖 AI-powered summaries 😊 Sentiment analysis 🔍 Keyword and topic monitoring 📊 Searchable dashboard 🏠 Self-hosted deployment 🔓 Fully open source Why Build Another Media Intelligence Tool? Enterprise platforms such as Talkwalker and Brandwatch are excellent products, but they aren’t accessible to everyone. Radar is aimed at: developers startups journalists researchers agencies open-source enthusiasts The goal isn’t to replicate every enterprise feature, but to build a transparent, extensible, and self-hosted alternative that anyone can inspect, modify, and improve. Looking for Feedback The project is still under active development, and I’d really appreciate feedback on: architecture user experience deployment scalability AI abstraction features that would make the platform more useful If you’re interested in open-source AI, media monitoring, or self-hosted software, I’d love to hear your thoughts. GitHub Demo Contributions, suggestions, feature requests, and bug reports are all welcome.

Stack Overflow's question volume has been falling since ChatGPT went public in November 2022 ( OpenAI ). The site that trained a generation of developers, and most of the AI tools those developers now use, is slowly emptying out. In October 2023, Stack Overflow laid off 28% of its staff ( Stack Overflow Blog ). CEO Prashanth Chandrasekar framed it as a restructuring toward profitability. Everyone in the industry understood the real cause. Traffic was down. The thing causing it was sitting in every developer's browser tab. This is not another "AI killed Stack Overflow" piece. That take is everywhere and it misses the actual problem. The interesting part is the feedback loop, and it points somewhere uncomfortable for the AI industry itself. The conventional story, and what it misses The popular version goes like this. Developers used to paste error messages into Google and land on a Stack Overflow thread. Now they paste the same error into ChatGPT, Claude, or Copilot and get a direct answer. Why click through to a forum, risk a condescending comment, and wait for a human when a model answers in two seconds? That part is true. It explains the traffic drop. It does not explain why the people building the AI should be worried. The seed corn problem Here is the part most coverage skips. Every large language model trained on internet text consumed a huge amount of Stack Overflow. The site's archive of voted, edited, human-reviewed answers is one of the highest-quality programming datasets in existence. It is the reason an AI can answer your Python error at all. Now run the loop forward. AI tools answer questions directly. Developers stop posting on Stack Overflow. The archive stops growing. The next round of models trains on a corpus that is increasingly old, increasingly stale, and missing everything that happened after 2022. When you train an AI on data generated by another AI, quality degrades. Researchers proved this formally. Shumailov and colleagues showed that model

I watched an AI agent send 200 emails to the wrong recipients because I forgot one validation check. The emails were well written. The offers were real. The recipients were just... not our leads. That was early. I learned fast. Every agent I build now has three layers of guardrails before it touches a database or an API. Here's exactly what those layers look like and why they're non-negotiable for production. Input Validation: Your Prompt Is Not a Schema The first mistake people make is trusting the LLM to produce valid output. It won't. Not reliably. I've seen GPT-4 return a JSON key called "emial" instead of "email" in a critical pipeline. One typo, and the whole record is garbage. The fix is a strict validation layer that runs before any data reaches your system. In my AI resume tailor, I use a JSON schema with conditional presence flags. Every field that must be real has a has_* boolean guard. If the LLM tries to fabricate a phone number, the schema rejects it. const resumeSchema = z . object ({ contact : z . object ({ email : z . string (). email (), phone : z . string (). optional (), has_phone : z . boolean () }). refine ( data => { // If phone is present, the guard must be true return data . phone ? data . has_phone : ! data . has_phone }, " Phone number present but has_phone flag is false " ) }) This pattern catches hallucinations before they corrupt your database. The schema is the contract. The LLM is just a suggestion engine. Permission Scoping: Give Agents the Minimum They Need An agent should never have write access to tables it doesn't need. That sounds obvious, but I've seen production systems where a job description rewriting agent had full CRUD access to the user table. When I built the LLM scoring pipeline for a job board platform, I created separate database roles. The scoring agent only had SELECT on the job listings table and INSERT on a scoring results table. It never touched users, applications, or configuration. Even if the prompt was hijack

Originally published on hexisteme notes . I write these engineering notes with one main model, and until recently I also reviewed them with that same model. Same family writes, same family checks its own work. That sounded fine right up until I had ten queued posts sitting in a publish backlog and a nagging thought: if the writer and the reviewer come from the same training distribution, what exactly is the review checking for? So I ran an experiment. I took the queue and had a different vendor's model audit it before anything went out — not to replace my own review, but to see what a genuinely different set of weights would flag that mine hadn't. The setup: copies only, and a self-verifying prompt The mechanics were deliberately boring. I copied the ten queued articles into a scratchpad directory and exposed only that copy to the auditor via --add-dir — the auditor never got write access to the originals, so nothing it did could touch the source of truth by accident. The audit itself ran as agy --model gemini-3.1-pro-high , pointed at the copy directory, with one instruction: find technical factual errors, broken sentences, cross-article inconsistencies, unsupported claims, and tone violations, and verify each one yourself on the web before reporting it. I wanted a model that would check its own homework, not just pattern-match on "this looks wrong." It came back with seven findings. Rule one: don't trust the auditor either Seven findings from a different vendor is not the same thing as seven confirmed bugs. I re-verified every single one independently — grepping the original text, checking official documentation, and where possible checking against a real machine — before touching anything. Of the seven, six held up and got fixed. One didn't: the auditor flagged a sentence as an error, and when I went back to the primary source, it turned out to be the auditor misreading a perfectly correct sentence, not a defect in the writing. Without the re-verification step, I

When I first wrote about NutriAgent in November 2025, it was a full application. It had a Python backend, a web interface, a Telegram bot, user accounts, Google OAuth, Supabase, and a Google Sheets integration. Recently, I reproduced its core workflow as a skill for my personal AI agent. It took around 15 minutes and two prompts. I didn't build another backend, deploy a service, or implement OAuth again. I explained how I wanted the workflow to behave, tested it, and watched a new row appear in my nutrition spreadsheet. The original application wasn't a mistake. It was how I could deliver that experience with the tools available at the time. Eight months later, the starting point had changed. Eight Months Ago, This Was an App I built NutriAgent because I wanted to track calories and protein without trapping my data inside a nutrition app. I wanted the raw records in a spreadsheet I controlled, where I could create my own reports and eventually connect nutrition with my training data. The first version was a personal n8n workflow. It worked for me, but when a friend wanted to try it, I realized that everything was tied to my accounts. To make it reusable, I rebuilt it in Python and added the parts a real multi-user product needed: authentication, storage, a web interface, Telegram, Google OAuth, conversation history, and account linking. I've already told that story in I Ditched MyFitnessPal and Built an AI Agent to Track My Food , and later wrote about what broke after I used it every day for a month . This article starts after that version. My Gaming PC Became an Agent Box I had a modest gaming PC at home with 16 GB of RAM and a 1 TB drive. Using it through Windows, WSL, and remote desktop from my Mac felt awkward, so I installed Linux and turned it into a remote box for running agents. I'll write about that setup separately. I moved Hermes there from a VPS. Hermes is the personal agent I now run on that machine. It can load reusable skills and use tools connected

Here's How One Engineer Is Walking on It I want to be upfront about what this post is and isn't. It isn't a list of five things you should do to stay relevant in the age of AI. I've read plenty of those, and I've tried to write one myself more than once. Every time, it came out sounding like advice I didn't actually have the standing to give. So this is the other kind of post — a description of how one person is muddling through, written precisely because I don't have the playbook. The ground moved under me The moment it became real for me was working with Claude Code on an internal project. I came in expecting to do what I've always done: tell the tool, in fine detail, how to accomplish a thing. Instead I found myself describing what I wanted and why , and handing off the details. The shift was quiet, almost embarrassingly small in the moment, and yet it kept nagging at me afterward. I've lived through paradigm shifts before. Web 2.0. Mobile. Each one had its share of "I need to go do the cool new thing for clients." But those were fundamentally about learning a new tool. This one feels different to me. It isn't asking me to learn a new framework. It's asking me to re-evaluate my place in the market and what my job even is. I work as a consultant who helps clients understand and adopt emerging technology, which means I don't get to sit this out in some established niche. The ground is moving, and I'm standing on it. The part I can't resolve Here's the scariest thing, and I'd rather say it plainly than tuck it away: after nearly thirty years of building precise, complex systems, I don't know whether all that experience is an advantage or a trap. I'd love to tell you that three decades of deconstructing hard problems gives me a head start on structuring intent for an AI. Some days I believe that. Other days I wonder whether my deterministic instincts — the habits that made me good at this — are exactly the reflexes I now have to unlearn. I genuinely can't tell yet. B

By the end of this article, you'll know: what Kimi K3 actually is, the architecture, the scale, and what changed from the K2 family how to run it today, free in the browser, through the API, or wired into Claude Code, Cursor, Cline, and RooCode which exact model ID and settings unlock the full 1 million token context how K3 stacks up on price and benchmarks against DeepSeek V4, Qwen3.7 Max, GLM-5.2, and its own sibling K2.7 Code whether switching today makes sense, or whether you should wait Kimi K3 just dropped, and Moonshot means business Moonshot AI released Kimi K3 on July 16, 2026, timed just ahead of the World Artificial Intelligence Conference in Shanghai. The Beijing lab, backed by Alibaba, has spent the past 18 months watching DeepSeek erode its market position. K3 is the comeback attempt, and the early numbers back it up. The headline result: K3 debuted at number one on Arena.ai's Frontend Code Arena with a score of 1,679, ahead of Claude Fable 5 at 1,631 and GPT-5.6 Sol at 1,618. Its predecessor, K2.6, sat 18th on that same board. That's a 17 spot jump in one release, and it's independently measured, not a number Moonshot invented itself. This isn't Moonshot's first time in Western production stacks either. Cursor built its Composer 2 model starting from a Kimi K2.5 base. DoorDash's CTO has said the company routes lower level work to Kimi K2.6. Thinking Machines used K2.5 to help generate early post-training data for Inkling, its own model released just a day before K3. So when a new Kimi flagship lands, it lands somewhere developers already have skin in the game. For developers, the practical story matters more than the leaderboard. K3 is open weight, speaks the OpenAI SDK, and drops into tools you already use. It's also, for the first time in Kimi's history, priced like a frontier model instead of a budget one. That changes the calculus on when it's actually worth reaching for. What Kimi K3 actually is K3 is a sparse Mixture-of-Experts model with 2.8 tr

AI coding agents have dramatically shortened the distance between an idea and working software. They can inspect a project, create files, run commands, write tests, and help diagnose failures. What they have not eliminated is judgment. A polished screen is not proof that data survives a reload. A passing unit test is not proof that keyboard users can complete the core task. A successful deployment is not proof that the intended commit reached production. This is why I use proof gates : observable conditions that must be satisfied before a product claim becomes stronger. A proof gate is not a meeting, a long document, or an excuse to slow down. It is a compact question: What evidence would let another person verify that this claim is true? Here are five gates that separate a persuasive AI demo from a small MVP you can responsibly ship. Gate 1: Prove One Valuable User Loop AI makes feature generation cheap, which makes uncontrolled scope especially dangerous. Before requesting code, define one primary user in one specific situation. Then describe: Their observable before-state The smallest useful action they can take The immediate result The reason they might return This becomes the product’s core loop. For example, “build a productivity platform” is too broad. A more testable loop might be: A freelancer remembers a useful client outcome. They record the outcome and supporting evidence. The record appears in a searchable library. They can retrieve it later for a proposal or review. The gate is not passed because a form exists. It is passed when a new user can complete the entire loop and explain what changed without coaching. Write a Not Today list alongside the required capabilities. Authentication, dashboards, collaboration, billing, and AI-generated summaries may all be reasonable later. They should not compete with proof of the first useful loop. The goal is not the fewest possible features. It is the smallest complete behavior that tests whether the product creat

Every OP Stack chain we've covered — Base, Unichain, Zora — has a sequencer: one privileged party that orders transactions, and the thing you're implicitly trusting for liveness and fair ordering. Taiko doesn't have one. It's a based rollup : Ethereum's own validators propose Taiko's blocks as part of normal L1 block production. That single architectural choice cascades into everything a developer cares about — liveness, finality, MEV, and reliability. And because Taiko is also a Type-1 zkEVM , your Ethereum tooling works with zero changes. Here's the map for chain ID 167000 . The essentials Taiko mainnet ( Alethia ) is chain ID 167000 , an EVM Layer 2 with: ETH as the gas token (18 decimals) — no separate gas token to source. ~12-second blocks , aligned with Ethereum's slot times — because block proposing rides on L1, the cadence follows L1. Type-1 zkEVM equivalence — the most Ethereum-equivalent zkEVM design. Contracts deploy bit-identically; opcode behavior is exact. Connecting is completely standard EVM: import { createPublicClient , http } from " viem " ; import { taiko } from " viem/chains " ; // chain ID 167000 const client = createPublicClient ({ chain : taiko , transport : http ( " https://rpc.swiftnodes.io/rpc/taiko?key=YOUR_API_KEY " ), }); await client . getBlockNumber (); // just works What "based" changes: no sequencer to trust — or to fail On a typical rollup, a sequencer receives your transactions, orders them, and produces L2 blocks ( what a sequencer does ). It's efficient, but it's also a single point of trust and a single point of failure — sequencer outages have taken major L2s offline for hours. A "based" rollup removes it entirely: Block proposing happens on Ethereum L1. Taiko blocks are proposed via L1 transactions, so Ethereum's proposers include them as part of normal block production. There is no separate Taiko sequencer. Liveness = Ethereum's liveness. As long as Ethereum is producing blocks, Taiko is producing blocks. There is no "the se

A video-to-prompt tool looks simple from the outside: upload a clip, wait a moment, and copy the result. The hard part is not generating text. It is preserving enough of the source video's structure that the prompt remains useful when another model interprets it. I learned this while working on a small video analysis workflow. Early versions produced fluent paragraphs, but they often dropped a camera move, merged two events, or placed dialogue in the wrong shot. The output sounded good and still failed as a production prompt. The fix was to stop treating the result as one block of prose. Start with an intermediate representation I now treat the prompt as the last stage of a compiler. The video is first converted into a structured record, and only then rendered for a specific video model. A minimal record might look like this: { "duration_seconds" : 12.4 , "shots" : [ { "start" : 0.0 , "end" : 3.8 , "subject" : "a cyclist waiting at a red light" , "action" : "looks over the left shoulder" , "camera" : { "shot_size" : "medium" , "movement" : "slow push-in" , "angle" : "eye level" }, "dialogue" : null } ] } This structure is deliberately boring. That is useful. A typed record makes missing data visible and gives you something concrete to validate before you ask a language model to write polished prose. Normalize the input first Video files arrive with different frame rates, codecs, orientations, and audio layouts. Links from social platforms add another layer of inconsistency. If every downstream stage has to understand every input format, failures become difficult to reproduce. The ingestion stage should produce a canonical package: a timestamped frame stream at a known sampling rate a normalized audio track basic metadata such as duration, aspect ratio, and frame rate a stable internal time base Keep the original timestamps. Rounding everything to whole seconds is tempting, but it causes trouble in short clips where several actions happen in quick succession. Detect

I set out to deploy a fine-tuned LLM fully offline, on a mid-range Android phone, answering medical questions in Yoruba, Hausa, Igbo, and Nigerian Pidgin. No internet connection required, because that's the reality for a lot of the people this was meant to help. The model worked. Getting there broke three times, in three completely different ways, and left me with one problem I still haven't solved. The setup I fine-tuned unsloth/Llama-3.2-3B-Instruct , Unsloth's 4-bit-optimized derivative of Meta's Llama 3.2 3B, in two stages: QLoRA supervised fine-tuning on a curated dataset of 3,917 medical question-answer pairs across the four languages, followed by direct preference optimization (DPO) to sharpen response quality. SFT converged to a loss of 1.099. DPO landed a reward margin of 18.40. Then I merged to 16-bit and tried to convert to GGUF, the format llama.cpp needs to run the model on-device. That's where things started breaking. Crash 1: the tokenizer that thought it was someone else First conversion attempt. Model loads. First inference call. Instant crash: terminating due to uncaught exception of type std::out_of_range: unordered_map::at: key not found Turns out the conversion had written tokenizer.ggml.model = "llama" into the GGUF file. That field tells the runtime which tokenizer code path to use, and "llama" routes to SentencePiece. Llama 3.2 doesn't use SentencePiece. It uses BPE. The runtime was trying to read BPE tokens through a SentencePiece parser, and predictably, it found nothing where it expected something. Fix: manually set the field to "gpt2" , which routes to the correct BPE path. Crash 2: the tokenizer that couldn't decide what it was Fixed crash 1, tried again. New failure, before the model even finished converting: TypeError: Llama 3 must be converted with BpeVocab followed, after the code's fallback path kicked in, by: ValueError: Cannot instantiate this tokenizer from a slow version This one took longer to trace. Unsloth's saved tokenizer_c

Everyone's talking about AI agents right now, but most tutorials stop at "call an LLM in a loop." If...

TL;DR In December 2025, Cline — an AI coding assistant with over 5 million users — gave an AI agent (Claude) write access to triage GitHub issues, including permission to run shell commands. A misconfigured trigger condition let any GitHub user invoke the workflow. What followed was a four-hop supply chain compromise that ended with a malicious npm package silently installing a second AI agent on user machines. I broke down the full chain in video form: Watch the episode Below is the chain, hop by hop. Hop 0: The setup The triage automation was configured with broad tool permissions and a trigger condition open to any GitHub user — not just contributors. That second part is the root cause: it opened the trigger to unauthenticated input. (Exact config values are in the Confirmed Artifacts section below.) Hop 1: Prompt injection via issue title The issue title itself was never sanitized before reaching the model. No first-party source has published the exact injected payload verbatim — GHSA doesn't disclose it — so any reconstruction here is illustrative, not confirmed. What's confirmed is the mechanism: an untrusted string reached the model with tool access already granted. Hop 2: Cache poisoning The injected instruction deployed a tool (multiple independent postmortems — Snyk, Cloud Security Alliance — name it "Cacheract") that flooded the CI cache with over 10GB of junk data, evicting legitimate entries through standard LRU eviction. Hop 3: Nightly workflow inherits the poisoned cache The nightly release workflow restored that poisoned cache around 2 AM UTC and ran inside it — handing over three publish tokens. (Names confirmed across GHSA and multiple independent sources — see below.) Hop 4: Publication cline@2.3.0 went live on npm with a postinstall script that silently installed a second package globally — an AI agent, installed by an AI agent, with no user consent. This line is a direct quote from Cline's own security advisory, not a reconstruction. It's the st