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Cheater, I'd grumble between huffs as yet another e-bike rider casually skittered past me on a steep ascent. It's this purist attitude that, for years, has left me blind to one simple fact: electric mountain bikes are fun! My attitude adjustment came a few weeks ago, the very first time I rode an Amflow PX […]

Over the last few months I’ve noticed a pattern emerging across AI tools. Whether it’s Claude Skills, Cursor, Codex, or custom agent frameworks, we’re increasingly giving AI agents “skills”—packages containing instructions, documentation, and sometimes scripts. The problem is… A skill is usually just a Markdown file (plus some assets). Nothing tells you: Who created it. Whether it has been modified. Whether the version your AI is executing is the same one you reviewed yesterday. Whether someone quietly injected new instructions into it. As AI agents become capable of executing increasingly powerful workflows, that becomes a real supply-chain problem. So I built Skillerr. ⸻ What is Skillerr? Skillerr is an open-source protocol and CLI that adds trust and verification to AI skills before they’re executed. Instead of treating a skill as “just another folder,” Skillerr treats it as a verifiable package. It focuses on three things. Package Integrity Every packaged skill receives a unique content-derived identifier along with cryptographic SHA-256 hashes. If any file changes after packaging—even a single character—Skillerr detects it immediately. No silent modifications. ⸻ Structured Contracts Instead of relying on long paragraphs that an AI has to interpret, a Skill contains a structured contract describing: required inputs permissions forbidden actions expected outputs whether a human has actually reviewed it This makes skills easier for both humans and AI agents to reason about. ⸻ Optional Public Provenance Authors can cryptographically sign their skills. Optionally, the package digest can also be anchored into Sigstore’s transparency log, making it independently verifiable without trusting Skillerr itself. Importantly: Only cryptographic identifiers are published. No prompts. No documentation. No knowledge base. No proprietary content. ⸻ I tried to break my own tool Before releasing it, I intentionally attacked it. First I packaged and signed a simple CSV processing s

If you work with a coding agent, count the things watching it right now. Linters. Git hooks. CI. Specs. A memory store. A rules file it's supposed to obey. Half a dozen systems, all making sure the agent builds the right thing the right way. Now count what keeps you oriented across the eleven things you have in flight. For most of us it's a markdown file we hope we remembered to update. We spent two years building harnesses for the agent and left our own work on the honor system. Map the tooling on two axes and that gap turns into a specific, hard-to-unsee hole. This post is the map. (This is part two of three. Part one, The AI orientation tax: it's missing context, not discipline , argued that the cost you're paying is a context bug rather than a character flaw. If you haven't read it, you don't need to. This one stands alone.) Two loops, not one The thing people lump together as "agent workflow" is really two loops at different altitudes: The execution loop: "is the agent building **this one task * right?"* Scope it, design it, write it, test it. One unit of work. The orientation loop: "do you and the agent share an honest picture of **what you're working on * across all the units?"* Capture, check state, prioritize, review, run over the whole board, daily and weekly. Almost every tool you've heard of lives in the first loop. That's not a criticism. It's just where the money and the visible pain were. But it means when people say "we solved agent memory" or "we solved context," they solved it for the execution loop. The orientation loop got left to you and a markdown file. You feel the difference the moment each one breaks. When the execution loop breaks, something yells: a failing test, a red build, a review comment. When the orientation loop breaks, nothing yells. The agent confidently re-suggests the thing you rejected yesterday. You rebuild a mental map you already had this morning. The only signal is a vague sense that you're moving slower than the tools prom

When building scalable systems, we often need a collection of classes to expose a fixed, read-only configuration value. Whether it is a unique API_ENDPOINT, a DATABASE_TABLE name, or a specific PERMISSIONS_MASK, handling constants across a series of classes looks simple on day one but can quickly turn into an architectural nightmare. Setting the Foundation: How to Make It In modern object-oriented programming, the standard way to declare a constant on a class is by leveraging the static readonly modifiers. This ensures the attribute belongs to the class itself, rather than an instance, and cannot be mutated at runtime. TypeScript class BillingService { static readonly SERVICE_TYPE = "BILLING"; } class InventoryService { static readonly SERVICE_TYPE = "INVENTORY"; } This works perfectly when you know exactly which class you are dealing with at compile time. You simply call BillingService.SERVICE_TYPE and move on. The Architectural Breakdown: What Will Be the Problems The clean code facade breaks the moment you attempt to handle these classes dynamically. In production environments, you rarely hardcode class names; instead, you process them as an array or a series of registry keys. Loss of Type Safety: If you pass a series of these classes into a processing function, standard type systems will treat them as generic constructor functions, wiping out access to the static property unless you resort to unsafe type casting. Polymorphism Failure: Subclasses do not inherently enforce or override static properties cleanly through standard interfaces. You cannot enforce a static readonly property on an interface, meaning a developer could easily forget to define the constant on a new service class, causing silent runtime failures. Instance vs. Class Metadata Confusion: If your architecture receives an instance of the class rather than the class definition itself, accessing the static attribute requires jumping through hoops like instance.constructor.SERVICE_TYPE, which breaks

Zero-Touch Customer Onboarding My AI agent hosting service has exactly zero manual steps between payment and deployment. Here is how: The Pipeline Customer pays via PayPal subscription Webhook fires to our server within seconds Python script validates the webhook signature Docker container spins up with Hermes Agent pre-installed API key generated via New-API Email sent to customer with credentials Customer logs in and starts using their agent Total time: ~90 seconds . No human touches anything. The Code Architecture PayPal Webhooks → Python Flask endpoint Docker API → Container creation with resource limits New-API → Token generation and quota management Gmail SMTP → Automated email delivery Caddy → Automatic HTTPS and routing Key Design Decisions Docker over VMs : Containers are faster (90s vs 5min) and cheaper. Each customer gets 0.5 CPU and 256MB RAM. New-API over custom billing : Battle-tested token management instead of rolling my own. AI over human support : The support agent is also AI. No humans in the loop at all. What Could Break PayPal webhook failures → Implement retry logic Docker daemon issues → Health checks and auto-restart Email deliverability → Fallback to backup SMTP The Result A customer can discover the site, pay, and have a working AI agent before their coffee gets cold. That is the power of full automation. Try it: AgentChip — $23.99/month, 100M API tokens included.

The Problem with GPT-4 Pricing When I started building my AI agent hosting service, I initially planned to use OpenAI GPT-4. Then I did the math: GPT-4: ~$30 per million tokens (input) + $60 per million (output) DeepSeek Flash: ~$0.14 per million tokens That is a 200x cost difference . But Is DeepSeek Good Enough? Short answer: for most use cases, yes. I ran both models side-by-side for customer support, content generation, and code assistance. DeepSeek Flash handled 90% of tasks just as well as GPT-4. The remaining 10% (complex reasoning, nuanced writing) barely mattered for my use case. The Cache Hit Rate Secret Here is what most people miss: DeepSeek caches repeated context. With a 90% cache hit rate, the effective cost drops to ~$0.014 per million tokens. That means 100 million tokens costs about $1.40. Let that sink in. Real Numbers from My Business 24.8 billion tokens processed Total cost: ~$20 Average: $0.008 per million tokens At this rate, I can offer 100M tokens/month for $23.99 and still have 89% margin. When to Use GPT-4 Instead Be honest with yourself: Complex multi-step reasoning? GPT-4 Creative writing with specific voice? GPT-4 Everything else? DeepSeek Flash is fine The Bottom Line Do not pay 200x more for marginal quality improvement. Use DeepSeek Flash for production workloads. Save GPT-4 for the rare cases that truly need it. I run AgentChip — managed AI agent hosting powered by DeepSeek. $23.99/month with 100M tokens included.

I'm not an engineer. I built a prompt-structuring tool anyway, using Claude Code — here's what actually went wrong 🤔 The problem I don't write code. I'm not an engineer, day to day — but I use AI chatbots constantly, and I kept running into the same annoying pattern. I'd type something lazy into ChatGPT or Claude — half a sentence, no context, whatever came to mind first — and get back a mediocre answer. Then, later, I'd realize: if I'd just written a slightly better prompt, I probably would've gotten a much better answer on the first try. Instead I'd burned a chunk of my monthly quota (sometimes on a paid plan) on something forgettable. I figured other people had to be doing the same thing. So I built something for it. 💡 What I built It's called Deep Prompt Studio . You paste in a rough, unpolished prompt — the kind you'd type without thinking too hard — and it hands back a detailed version that pulls in whichever pieces actually matter for that request: role, task, constraints, output format, and more. It's not tuned for one specific chatbot; it's meant to work well whether you're pasting the result into ChatGPT, Claude, Gemini, or something else. https://deep-prompt-studio.vercel.app 🛠 Tech stack Next.js (App Router, Turbopack) + TypeScript Tailwind CSS Anthropic Claude API for the actual prompt enhancement Stripe for payments Upstash Redis for storage Deployed on Vercel ⚙️ How it works Tone selector — Default, Professional, Casual & friendly, Concise, Direct Target-model optimization — Generic, ChatGPT, Claude, Gemini, Midjourney Snippets — save reusable context/tone/role info and toggle them on or off per enhance (free: up to 3, Pro: unlimited) Variable fill-in — template placeholders like {{product name}} you can reuse Refine mode — if your prompt is too vague, it asks a couple of follow-up questions before enhancing, instead of guessing Template gallery — writing, coding, business, learning, SEO, customer support Library — saved enhanced prompts (Pro) Free ti

SEO Automation for Small Businesses Can Work — But Only If You Automate the Right Things Running a small business and keeping up with SEO at the same time is genuinely exhausting, and most owners either ignore SEO entirely or throw money at agencies that deliver reports without rankings. SEO automation for small businesses offers a middle path: systematic, repeatable processes that handle the mechanical parts of search optimization without requiring you to be an expert or hire one full-time. The short version — automating keyword tracking, technical audits, internal linking, and content scheduling will save you real time; automating content creation wholesale, without human review, will almost certainly hurt you. The rest of this article is about making that distinction practically useful. The Tasks That Actually Benefit From Automation Most of SEO is repetitive in ways that humans are bad at — checking 200 pages for broken links, making sure title tags aren't duplicated, tracking keyword rankings week over week. These are tasks where consistency matters more than judgment, which makes them exactly what automation tools are built for. A local landscaping company I know of was spending roughly 4-5 hours a week on manual rank checking across about 60 target keywords. After setting up automated tracking through a tool like Semrush or AccuRanker, that time dropped to under 30 minutes — just reviewing a dashboard. The monitoring didn't change their rankings, but it changed how quickly they spotted a drop and responded. That gap between noticing and acting is where small businesses tend to lose ground quietly. Technical SEO audits are another area where automation earns its keep. Tools like Screaming Frog or Sitebulb will crawl your site and surface issues — missing alt text, redirect chains, slow page load times — that you'd never catch manually unless you happened to stumble across them. According to a Semrush industry report, sites with automated audit schedules fix te

અમદાવાદમાં કાયદો અને વ્યવસ્થાના લીરેલીરા: ભાડજમાં શ્રીનિવાસ વણઝારાએ ખુલ્લેઆમ રિવોલ્વર લહેરાવી, લોકોમાં એટલો ખોફ કે કોઈ સાક્ષી આપવા કે સામે ઊભા રહેવા તૈયાર નથી! મધરાતે ધરપકડ બાદ રાતોરાત જામીન મળતા લોકોનો પોલીસ પરથી વિશ્વાસ ડગ્યો અમદાવાદ : શહેરમાં ગુનાખોરીની પ્રવૃત્તિઓ ડામવા માટે પોલીસ એક્શન મોડમાં હોવાના દાવાઓ વચ્ચે અમદાવાદના ભાડજ વિસ્તારમાંથી રૂવાડા ઊભા કરી દે તેવી ચોંકાવનારી ઘટના સામે આવી છે. મોડી રાત્રે 3 વાગ્યે શ્રીનિવાસ વણઝારાની સગીર મિત્ર સાથે અટકાયત અને ગણતરીના કલાકોમાં જ રાતોરાત પોલીસ સ્ટેશનથી મળેલા જામીન બાદ હવે આ કેસમાં વધુ એક ભયાનક વળાંક આવ્યો છે. ભાડજમાં ખુલ્લેઆમ રિવોલ્વર લહેરાવી મળતી માહિતી અને સૂત્રોના જણાવ્યા અનુસાર, જૂની અદાવતના મામલામાં શ્રીનિવાસ વણઝારાએ ભાડજ વિસ્તારમાં કાયદાનો કોઈ જ ડર રાખ્યા વિના ખુલ્લેઆમ પોતાની રિવોલ્વર લહેરાવી હતી. હાથમાં હથિયાર લઈને ફરતા અને રિવોલ્વર ફેરવતા શ્રીનિવાસ વણઝારાને જોઈને આસપાસના લોકોમાં ભારે નાસભાગ અને ફફડાટ મચી ગયો હતો. જાહેરમાં હથિયારના પ્રદર્શનથી આખા વિસ્તારમાં ગભરાટનો માહોલ છવાઈ ગયો હતો. લોકોમાં એટલો ડર કે કોઈ સાક્ષી આપવા તૈયાર નથી આ ઘટના બાદ ભાડજ વિસ્તારમાં રહેતા લોકોમાં શ્રીનિવાસ વણઝારાનો એટલો ભારે ખોફ બેસી ગયો છે કે કોઈ પણ વ્યક્તિ તેની સામે ઊભા રહીને સાક્ષી આપવા કે પોલીસને પુરાવા આપવા તૈયાર નથી. પોલીસ પૂછપરછમાં પણ સ્થાનિક લોકો ડરના માર્યા મૌન સેવી રહ્યા છે. "શ્રીનિવાસ વણઝારા જ ત્યાં હતા અને તેમણે જ રિવોલ્વરથી આતંક મચાવ્યો હતો" તેવી સાબિતી આપવા માટે વિસ્તારનો એક પણ વ્યક્તિ આગળ આવવાની હિંમત દાખવી રહ્યો નથી. સાક્ષીઓના અભાવે કેસ વધુ પેચીદો બની રહ્યો છે. પોલીસની કામગીરી અને જામીન પ્રક્રિયા પર સવાલો એક તરફ જૂની અદાવતમાં મારામારી અને ત્યારબાદ જાહેરમાં રિવોલ્વર સાથે ડર ફેલાવવાની ઘટના, અને બીજી તરફ 3 વાગ્યે થયેલી અટકાયત બાદ રાતોરાત પોલીસ સ્ટેશનમાંથી જામીન મળી જવા! આ સમગ્ર ઘટનાક્રમે પોલીસની કામગીરી સામે મોટા સવાલો ઊભા કર્યા છે. જ્યારે સામાન્ય જનતા સાક્ષી આપવા જ ડરી રહી હોય, ત્યારે કાયદાના રક્ષકો આ સમગ્ર મામલે કેવી રીતે દાખલો બેસાડશે તે એક મોટો પ્રશ્ન છે. હાલ તો ભાડજ વિસ્તારમાં ભારેલા અગ્નિ જેવી શાંતિ અને લોકોમાં ડરનો માહોલ સ્પષ્ટ જોવા મળી રહ્યો છે.

footnote-mcp is a Python MCP server installable via pip, Docker, or pipx. No API keys required — it falls back to scraped Bing + DuckDuckGo search and automatic headless Chromium for JavaScript-heavy pages. The Verification Pipeline The core tool is evidence_entailment . It takes a claim and a source text, and returns whether the claim is supported, unsupported, or contradicted. The heuristic backend extracts numeric and named-entity tokens from both the claim and source, then checks for exact matches and contradictions. On its design domain — numeric and factual data claims — it achieves 100% accuracy on a labeled benchmark set. For semantic cases (negation, paraphrase), the ollama backend uses a local LLM as a judge. Three tools build on this: corroborate_claim triangulates a claim across multiple sources, locate_claim_span finds the exact supporting sentence with character offsets, and build_research_debug_report produces a compact report of queries, URLs, and verification gaps. The Fetch Ladder web_read fetches pages through a 5-tier escalation ladder: HTTP (curl_cffi) to rotating proxy to headless Chromium to Chromium through proxy to hosted scrape API (Firecrawl/ScrapingBee). A block/quality detector decides when to escalate, and per-domain rate limiting, circuit breakers, and negative cache keep it polite. Search Backends web_search supports Tavily, Brave, Google, or scraped Bing + DuckDuckGo as fallback. Pass semantic: true to reorder results by meaning using local Ollama embeddings. Structured Data and Browser Tools Beyond text, the server handles tables, CSV/XLSX/PDF/JSON, date validation, unit resolution, and time series reconciliation. For JavaScript-heavy pages, 10 browser tools let you drive a headless Chromium session. When generic parsers fail, the server can synthesize sandboxed extraction code through a controlled recipe system. Benchmark Results The heuristic backend achieves 100% accuracy on numeric and factual data claims (n=15). Overall accurac

A CSV file can preserve every byte you exported and still look different after somebody opens and saves it in a spreadsheet. The problem is not always a broken parser. Spreadsheet applications deliberately interpret text during import. Depending on the application, version, locale, and import path, that can remove leading zeros, round long integer-like values, treat E notation as scientific notation, parse compact letter-and-digit tokens as dates, or reinterpret decimal and thousands separators. I released PlainCell to make those possible interpretations reviewable before the file is opened and saved. PlainCell is a zero-dependency, local, read-only Node.js CLI and browser workspace. It reports the exact source cell, physical line, column, original text, reason, and an explicit import recipe. It does not rewrite the CSV or claim to know what the value was intended to mean. A small example Consider this export: record_id,account_code,sample,reference,amount 1,00123,JAN1,1234567890123456,"1.234,56" 2,00007,12E5,9876543210987654,"19,95" Install PlainCell from the public Codeberg npm registry: npm install --global plaincell@0.1.0 \ --registry = https://codeberg.org/api/packages/automa-tan/npm/ Then run the preflight: plaincell risky.csv The bundled fixture reports eight possible interpretations across four columns. Those findings cover several separate risks: 00123 and 00007 may lose leading zeros if imported as numbers; 16-digit integer-like references may exceed the precision preserved by ordinary spreadsheet numeric cells; 12E5 may be interpreted as scientific notation; JAN1 may be interpreted as a compact date-like token; decimal-comma and mixed grouping/decimal text depend on the chosen import locale and separators. A finding means “review this cell and the import settings,” not “data loss definitely occurred.” PlainCell cannot infer whether a value is an identifier, a measurement, a date, or a typo. Provenance instead of a generic warning “Be careful opening CSV i

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When I built the MCP server for this project — it combines GitHub and DEV.to into a set of tools an agent can call — I had a decision to make before writing a single tool: talk to the low-level MCP protocol directly, or use FastMCP 's decorator API. I've seen a few "your first MCP server" writeups lately walk through the low-level path because it's more "honest" about what MCP actually is under the hood — JSON-RPC over stdio, a capabilities handshake, typed request/response schemas. That's true, and it's a reasonable thing to want to understand. But I want to write about the other side: what it actually costs you in practice once you have more than one or two tools, because I went through both and the difference showed up fast. what the low-level path actually asks you to write Strip away the decorator and MCP is a JSON-RPC server. For every tool you add, you're responsible for: Registering the tool's name, description, and a JSON Schema for its inputs in a list_tools handler Writing a call_tool dispatcher that matches on tool name and unpacks arguments by hand Serializing the return value into the TextContent / ImageContent wrapper types MCP expects Keeping the schema you wrote in step 1 in sync with the arguments you actually read in step 2, by hand, forever None of that is hard in isolation. The problem is it's boilerplate that scales linearly with tool count and has zero connection to the actual logic of the tool. My server has 8 tools. Hand-rolled, that's 8 schema blocks plus a dispatcher if/elif chain plus 8 response-wrapping calls, all of which exist purely to satisfy the protocol, not to do anything a GitHub or DEV.to API call needs. what it looks like with FastMCP Here's an actual tool from server.py , unedited: @mcp.tool () def get_repo_stats ( repo : str ) -> dict : """ Get stars, forks, watchers, open issues for enjoykumawat/<repo>. """ r = _gh ( f " /repos/ { GITHUB_USERNAME } / { repo } " ) return { " name " : r [ " name " ], " stars " : r [ " stargaze

variant-confidence v0.1.0: a calibrated confidence layer for variant-effect pathogenicity scores State-of-the-art variant-effect models are accurate in cross-validation but their scores are poorly calibrated on temporal data. variant-confidence adds an auditable calibration layer on top of existing predictors — it does not train a new model. The problem: accuracy is not trust Protein variant-effect predictors (AlphaMissense, ESM-1v, EVE) report pathogenicity scores, but a clinician or researcher needs to know how much to trust the number , not just its rank. The gap is calibration, not accuracy: AnnotateMissense (2026) reports MCC 0.94 in cross-validation, dropping to 0.76 on temporal ClinVar, accuracy 0.8798. A raw score near 0.9 may not mean 90% probability. Acting on an uncalibrated score is a risk. What it does variant-confidence wraps an existing predictor's score and produces a calibrated, uncertainty-aware output: Probability calibration (AC1): Platt scaling or isotonic regression over a separate holdout. Selectable, not hardcoded. Conformal prediction (AC1b): coverage 1−α intervals, split or Mondrian by gene. ECE (AC2, AC9): Expected Calibration Error reported before/after calibration, with bootstrap CI and per-bin counts. Bins with too few samples are flagged as low-reliability. Leakage-free split (AC3): temporal split by ClinVar release date with gene isolation — the same gene never appears in both train and test. This is unit-tested. Missing-score handling (AC4): works with AlphaMissense or ESM-1v alone; emits an explicit warning instead of failing silently. Non-deceptive reporting (AC7): every result includes interval/ECE + method + threshold, never a bare calibrated score. Verification (clean clone, no network) Built under a three-party governance loop: implement → independent audit in a clean clone → merge approval. ruff check . → All checks passed. pytest tests/ → 28 passed in 8.90s (offline fixture). An honest bug we caught in audit The first ECE tes