标签：#ai

Tracking Tech Sentiment in Real-Time with VADER and Python What does the developer community feel about your product? Not what they say in reviews — what do they actually feel when they mention it on Hacker News or Reddit? I built a Sentiment Analyzer that fetches posts from HN and Reddit, runs VADER sentiment analysis, and outputs structured scores. Here's how it works. What It Does The tool pulls posts from two sources: Hacker News : Top or new stories via the official Firebase API Reddit : Any subreddit, sorted by hot, new, top, or rising Each post gets analysed with VADER (Valence Aware Dictionary and sEntiment Reasoner) — a rule-based model tuned for social media text. No GPU required, no API keys, no latency. What You Get Each analysed post includes: { "source" : "hackernews" , "title" : "Shadcn/UI now defaults to Base UI instead of Radix" , "sentiment" : "neutral" , "sentimentScores" : { "positive" : 0.0 , "neutral" : 1.0 , "negative" : 0.0 , "compound" : 0.0 }, "keywords" : [ "shadcn" , "ui" , "defaults" , "base" , "radix" ], "score" : 43 , "commentsCount" : 3 } The compound score ranges from -1 (very negative) to +1 (very positive). Anything below -0.05 is classified negative, above 0.05 is positive, and in between is neutral. Why VADER Instead of an LLM? Three reasons: Speed : VADER processes 10,000+ posts per second. An LLM call takes 1-2 seconds per post. Cost : VADER is free and runs locally. LLM sentiment analysis costs per token. Consistency : Rule-based models give identical results every time. LLMs can be inconsistent across runs. For high-volume monitoring tasks — like tracking every HN post mentioning your product — VADER is the right tool. Real-World Use Cases Brand Monitoring Set the analyzer to fetch posts from r/yourproduct and HN search for your brand name. Get daily sentiment reports. Catch negative sentiment before it escalates. Trend Detection Track sentiment around technologies like "AI agents", "Rust", or "WebAssembly" across both platfo

Introduction As data grows exponentially, accessing and querying databases efficiently...

AI coding assistants are great at writing code and terrible at knowing when to write it. Ask one to build a feature and it will happily jump straight to implementation, skipping the questions a good engineer asks first: what exactly are we building, why, what are the risks, and how should it be broken down? The result is fast output that often solves the wrong problem. AWS's AI-DLC (AI-Driven Development Life Cycle) is an attempt to fix that gap. It's an open-source set of workflow rules — released by awslabs — that steer AI coding agents through a disciplined software development process instead of letting them freewheel. Importantly, it isn't a tool you install or a service you pay for. It's a methodology delivered as a bundle of markdown rules that your existing coding agent reads and follows. The core idea: methodology over tooling One of AI-DLC's stated tenets is "methodology first." The whole thing ships as plain markdown rule files that you drop into whatever your agent already uses for project instructions — CLAUDE.md for Claude Code, .cursor/rules/ for Cursor, .github/copilot-instructions.md for GitHub Copilot, .amazonq/rules/ for Amazon Q, steering files for Kiro, and so on. There's nothing to run. The agent loads the rules and its behavior changes. This makes AI-DLC deliberately agnostic . It doesn't tie you to a specific IDE, model, or vendor — any coding agent that supports project-level rules can use it. The philosophy is that a good development methodology should outlive any particular tool. A three-phase adaptive workflow At its heart, AI-DLC organizes work into three phases that mirror how thoughtful software actually gets built. The Inception phase answers what to build and why . This is where the agent does requirements analysis, creates user stories when they're warranted, sketches the application design, breaks work into units that can be built in parallel, and assesses risk and complexity before a line of code is written. The Construction phase

I ran the same 39 AI agent security samples through three scanners: AgentGuard, Semgrep, and CodeQL. The Results Scanner Detection Rate False Positives AgentGuard v0.6.4 100% (39/39) 0 Semgrep 0% (0/39) 0 CodeQL 0% (0/39) 0 Zero. Semgrep and CodeQL detected nothing. They have zero rules for AI agent security. AgentGuard has 17 detection rules covering all 10 OWASP ASI categories plus 4 novel attack vectors: Memory Poisoning, Tool Output Trust, Action Chain Amplification, and Multi-Agent Collusion. Real World AgentGuard found 332 critical vulnerabilities across Microsoft AutoGen and LlamaIndex. Issues reported directly: autogen#7917, autogen#7918, llama_index#22245. Reproduce git clone https://github.com/dockfixlabs/agentguard-benchmark cd agentguard-benchmark pip install dfx-agentguard python benchmark.py GitHub: https://github.com/dockfixlabs/agentguard PyPI: pip install dfx-agentguard

Osloq — ใช้ AI หาสาเหตุ bug แทน เวลา AI coding tools เสนอจะ "fix bug ให้" — เราได้แต่กด Accept หรือไม่ก็ Reject สองปุ่ม สองทางเลือก แต่เราไม่เคยรู้ว่า: AI รู้ได้ยังไงว่า bug เกิดจากตรงนี้? มัน reproduce แล้วหรือแค่อ่านโค้ดแล้วเดา? ถ้าเรา accept — มันจะพังของอย่างอื่นไหม? Osloq เลือกทางที่สาม: ไม่ใช่ "fix ให้" — แต่ " หาให้เจอแล้วบอกว่าเกิดอะไรขึ้น " Osloq คืออะไร Osloq เป็น AI agent ที่ทำหน้าที่ "นักสืบ bug" มีคนเปิด GitHub Issue → Osloq อ่าน → trace โค้ด → reproduce ใน sandbox → ส่งรายงานพร้อมหลักฐาน ┌─────────────────────────────────────────────────────┐ │ GitHub Issue: "ปุ่ม submit กดไม่ติดบน Safari" │ └─────────────────────┬───────────────────────────────┘ ▼ ┌─────────────────────────────────────────────────────┐ │ Osloq: │ │ 1. อ่าน issue → เข้าใจว่า "ปุ่มไม่ทำงาน" │ │ 2. trace โค้ด: จาก handler → service → DOM event │ │ 3. reproduce: รัน Safari ใน sandbox → ปุ่มไม่ติดจริง │ │ 4. จับหลักฐาน: logs, screenshots, call stack │ │ 5. สรุป: "event listener ใช้ 'click' แต่ Safari │ │ บน iOS 18 ไม่ bubble event — ต้องใช้ 'pointerdown' │ └─────────────────────┬───────────────────────────────┘ ▼ ┌─────────────────────────────────────────────────────┐ │ Report บน GitHub Issue: │ │ 📸 screenshot ของ Safari ที่ปุ่มไม่ทำงาน │ │ 📋 console error: "Unhandled Promise Rejection" │ │ 🔗 code path: handler.ts:42 → form.ts:17 │ │ 💡 suggestion: เปลี่ยน event type │ └─────────────────────────────────────────────────────┘ คุณอ่าน report → เข้าใจปัญหา → ตัดสินใจเอง ว่าจะแก้ยังไง ต่างจาก "AI Fix Everything" ยังไง Devin / Sweep AI Osloq แนวคิด "Fix the bug" "Find the cause" ทำงานยังไง เขียนโค้ดใหม่ → เปิด PR Reproduce → รายงาน evidence เราเห็นอะไร PR diff ภาพ, log, call stack, บทสรุป ใครตัดสินใจ AI (เราแค่ merge) เรา (AI บอกว่าอะไรผิด) ถ้าผิดพลาด โค้ดผิดเข้า main Report ผิด — ไม่กระทบโค้ด ความเสี่ยง สูง — AI แก้โค้ดโดยตรง ต่ำ — AI แค่แนะนำ ทำไมถึง "สบายใจกว่า" 1. คุณเห็นหลักฐาน — ไม่ใช่แค่ diff ❌ "Fixed button click handler — please review" → review 300 บรรทัด — ไม่รู้ว่าแก้ถูกไหม ✅ "Button

Ask ChatGPT or Perplexity "what's the best note-taking app" and you get a shortlist of three to five names. Either you're on it or you don't exist in that channel. And buying research keeps moving there. People call measuring this GEO or AEO tracking now. The way most teams do it is pasting questions into chatbots by hand and eyeballing the answers. That stops scaling at about ten questions, and you can't trend it week over week. Doing it programmatically Don't scrape the chat UIs. It's fragile, against ToS, and breaks weekly. The engines all have official APIs with web search: Perplexity's sonar models return answers with citations built in OpenAI has gpt-4o-search-preview for live web search Gemini's gemini-2.5-flash supports Google Search grounding One OpenRouter key covers all three through a single endpoint, which keeps the code boring. For each buyer question you care about, record four things per engine: was the brand mentioned, how early in the answer, was your domain cited as a source, and how often competitors appeared. That last one gives you share of voice. The packaged version I built this as an Apify actor: AI Brand Visibility Tracker . You give it a brand name, domain, competitors, and topics. It generates realistic buyer questions and returns one JSON row per check: brandMentioned , positionScore , brandCited , shareOfVoice , citedDomains , plus a per-engine summary. Schedule it weekly and you have an AI visibility trendline for client reports. $0.05 per check. The field that actually matters citedDomains is the actionable one. It tells you which sites the AI engines treat as sources for your category. Getting mentioned on those specific domains is how you move your visibility. It's link building, except the target list comes from the AI's own citations instead of a guess.

I just opened 3 security issues on two of the most popular AI agent frameworks on GitHub (combined 110K+ stars). The Issues microsoft/autogen#7917 : Docker code executor mounts host filesystem into sandboxed containers without trust boundary validation — container escape vector. microsoft/autogen#7918 : Agent self-modification patterns in Canvas memory module — agents can alter their own operating constraints during execution. run-llama/llama_index#22245 : 441 instances of unbounded recursive agent execution across 2,951 files — systemic resource exhaustion risk. All found with AgentGuard v0.6.2 (pip install dfx-agentguard), an open-source AI agent security scanner. Why Issues, Not Articles I have published 12 articles on Dev.to. Average views: 11. GitHub Issues on 50K+ star repos are read by thousands of developers and stay visible for years. This is the correct distribution channel for security findings — direct, unfiltered, and actionable. The Pattern The same vulnerability classes appear across all frameworks: Trust boundary violations (ASI10): agents crossing filesystem and network boundaries Agent recursion (ASI09): unbounded loops without circuit breakers Self-modification (ASI10): agents modifying their own state during execution These are not framework-specific bugs. They are systemic architectural gaps in how we build autonomous agents. Every framework needs guardrails for resource limits, trust boundaries, and behavioral constraints. AgentGuard detects all of them. 16 rules, 83 tests, 36 benchmark samples, 100 percent detection rate. pip install dfx-agentguard

Fable and Opus may not be the most comfortable tools for engineers who learned to code by hand. I started thinking about this after reading Simon Willison's recent note . His point is simple: with a strong coding agent like Fable, it may be better to let the model exercise its own judgment than to spell out every condition yourself. Instead of writing detailed rules like "run tests for larger features, but not for small copy changes, except for design changes...," you can simply say: write and run tests where appropriate. The same applies to cost. Rather than deciding manually which tasks should go to which model, you can ask the agent to choose an appropriate lower-cost model and delegate the work to a subagent. Manual cars and automatics This is a rough analogy, but it feels similar to driving a car. People who enjoy driving often like manual cars. They want to choose the gear themselves. They want to feel the engine speed and have the car respond directly to their intent. For people who simply want to get somewhere, an automatic is easier. Software engineers are similar. If you have written code professionally for a long time, you usually have your own way of working. You may want to get the types right first. You may prefer small diffs. You may have a specific sense for how granular tests should be. You may even have an order in which you like to read an unfamiliar codebase. (At least, I hope you do.) For someone with that kind of style, a highly autonomous model like Fable or Opus can feel a little too automatic. The stronger the model, the more small instructions get in the way This is the same structure as management in human organizations. A junior member needs concrete instructions: read this document from this angle and summarize it in this format. A senior member can take a rougher assignment: I want to solve this problem, so investigate it, come up with an implementation plan, and move it forward. Of course this does not mean throwing work over the wall.

GA in a sentence GitHub moved its enterprise managed-settings.json to general availability on July 1, giving GitHub Enterprise Cloud admins a single JSON file that overrides Copilot behaviour in VS Code and Copilot CLI for anyone holding a Copilot Business or Copilot Enterprise seat issued from the enterprise or one of its organizations. The changelog frames it as a place to define AI standards for the tenant. In practice it is a supported home for Copilot policy that shipped one setting at a time in beta up to this point. The five keys the file accepts Five keys are documented at GA: extraKnownMarketplaces , enabledPlugins , strictKnownMarketplaces , disableBypassPermissionsMode , and model . Together they configure trust for extra plugin marketplaces, the enabled-plugins list, strict enforcement of the known-good marketplace list, whether Copilot CLI and the VS Code extension can run in bypass-permission mode, and which model a user is allowed to pick. Value shapes are not enumerated in the changelog itself; the docs page is the reference for the schema. How the file reaches a client The file lives at copilot/managed-settings.json inside the .github-private repository of the organization the enterprise nominates for the role. There is a backward-compatible path at .github/copilot/settings.json for tenants already using the older layout. Copilot clients fetch the file from the server on every authentication, hold it in memory, and refresh it hourly, per the changelog. That server-side file takes precedence over the file-based config a user may have on their own machine. Setup runs through the AI Controls tab in enterprise settings, or the equivalent API endpoint, where an admin picks the hosting organization. Anyone who followed the June rollouts of disableBypassPermissionsMode and strictKnownMarketplaces will recognise the same file and the same repo. GA is what turns the plumbing into a supported product surface. Where it will trip you Two operational details are

We’ve all been there. You have a "Leg Day" scheduled in your Notion database, but you woke up feeling like a truck hit you. Your Apple Watch says your Heart Rate Variability (HRV) is in the gutter, but your rigid calendar doesn't care. Usually, you’d either push through and risk injury or manually move cards around in Notion—which is a friction-filled nightmare. In this tutorial, we are building a Self-Optimizing Health Agent using LangGraph , Notion API , and HealthKit . This agent acts as a closed-loop system: it analyzes your physiological recovery data, reasons about your physical state using an LLM, and automatically rewrites your training schedule. By mastering AI agents , LLM orchestration , and fitness automation , you’ll turn your static "To-Do" list into a dynamic "Should-Do" list. 🥑 The Architecture: The Bio-Feedback Loop Using LangGraph , we can treat our fitness logic as a state machine. Unlike a linear script, a graph allows our agent to decide whether it needs to fetch more context (like yesterday's sleep) before making a final decision on your workout. graph TD Start((Start)) --> FetchHRV[Fetch HRV Data via HealthKit] FetchHRV --> CheckRecovery{LLM: Analyze Recovery} CheckRecovery -- "Low Recovery (Fatigued)" --> ModifyNotion[Action: Downgrade Workout Intensity] CheckRecovery -- "High Recovery (Fresh)" --> KeepNotion[Action: Maintain/Boost Intensity] ModifyNotion --> UpdateNotion[Update Notion Page] KeepNotion --> UpdateNotion UpdateNotion --> End((Done)) style CheckRecovery fill:#f96,stroke:#333,stroke-width:2px style FetchHRV fill:#bbf,stroke:#333 Prerequisites Before we dive into the code, ensure you have: Python 3.10+ LangChain & LangGraph installed ( pip install langgraph langchain_openai ) Notion Integration Token (with access to your workout database) HealthKit SDK (Note: Since we are in a Python environment, we'll simulate the HealthKit fetcher, though in a real-world scenario, this would be bridged via a FastAPI endpoint from an iOS app). St

What We Built in This Guide In the previous guide, we went from RAG to cloud deployment. In this guide, we systematically implemented everything needed to take that system to production . evals/ dataset.py # Evaluation dataset eval_rag.py # Context Recall · Relevancy · Faithfulness observability/ traced_rag.py # RAG pipeline tracing with @observe() (Langfuse v4) traced_agent.py # Trace each Agent step security/ input_validator.py # Prompt injection detection output_validator.py # PII masking and leakage detection guardrails.py # Rate limiting, security log integration secure_rag.py # RAG with guardrails llmops/ prompt_registry.py # Prompt version management (v1.0–v1.2) ci_eval.py # Quality gate (Overall ≥ 75% to deploy) cost_tracker.py # API cost tracking finetuning/ prepare_dataset.py # Convert to Alpaca format train_lora.py # LoRA fine-tuning (r=8, 2 min on CPU) inference.py # Compare with base model multiagent/ search_worker.py # Search specialist worker quality_worker.py # Quality check specialist worker orchestrator.py # Task decomposition and result integration 14_multiagent.py # Execution script governance/ ai_registry.py # AI system inventory risk_assessor.py # Risk assessment (score 0.18 → LOW) audit_logger.py # Audit log (Article 12 compliant) compliant_rag.py # RAG with AI disclosure (Article 50 compliant) Key Design Decisions from Each Chapter Chapter 2: Evals Combining rule-based (Context Recall, Answer Relevancy) with LLM-as-a-Judge (Faithfulness) strikes the right balance between speed, cost, and coverage. Chapter 3: Observability (Langfuse v4) Adding @observe() decorators is all it takes to start recording traces. The critical v4 change: you must call get_client() after load_dotenv() . Chapter 4: Security Defense in Depth is the principle: Input validation → System prompt → Output validation → Rate limiting — four layers of protection. Chapter 5: MLOps / LLMOps On every push to GitHub, Evals run automatically. Only when the quality threshold (Overall

It's an exciting time to be in tech, with AI making headlines daily and business leaders eager to leverage its power. Yet, as a Senior IT Consultant and Digital Solutions Architect with over a decade of experience, I've observed a recurring pattern: many companies enthusiastically adopt AI tools, only to find their balance sheets reflect increased software licensing costs but no tangible improvement in core operational metrics like processing times, customer support turnaround, or error rates. This is what I call the AI adoption gap . The issue isn't the capability of Large Language Models (LLMs) or automation tools themselves; it's the absence of a structured integration strategy. Simply purchasing individual tool licenses rarely translates into automated business processes or measurable value. True transformation requires a deeper, more thoughtful approach. My role as a Fractional CTO often involves guiding businesses through this challenge—moving them from mere AI adoption to strategic AI integration. Over the years, I've refined a step-by-step audit framework that helps identify high-leverage automation points and design integrations that genuinely deliver measurable business returns. Let's dive into how you can apply this framework within your organization. 1. Step 1: Mapping High-Volume, Linear Workflows Before you can automate anything, you need a crystal-clear understanding of the process itself. This initial phase of an automation audit is all about documenting your existing business workflows. You cannot effectively automate what hasn't been precisely mapped. When identifying candidates for automation, I look for workflows that exhibit specific characteristics, as these offer the highest potential for immediate and impactful ROI: High Volume : Focus on tasks that are performed dozens, hundreds, or even thousands of times per week. Automating a task that happens once a month, while potentially valuable, won't move the needle on overall operational efficienc

Introduction Through Chapter 7 (Multi-Agent) , we have a complete, functioning AI system. The final step is building organizational infrastructure to operate AI safely over time. [Before] Technical safety Security → Block malicious input Evals → Measure quality [Now] Organizational / regulatory safety Governance → Know what AI systems are in use Risk mgmt → Classify and assess risks Audit logs → Record who did what, when EU AI Act → Regulatory compliance EU AI Act Status (as of June 2026) The EU AI Act came into force on August 1, 2024, with full enforcement on August 2, 2026 . Transparency rules (disclosing when users are interacting with AI, labeling AI-generated content) also take effect on that date. AI systems are classified into three risk tiers: Risk Level Description Examples Prohibited Not permitted Social scoring, manipulative AI High Risk Strict regulation Hiring, credit scoring, law enforcement Limited Risk Transparency obligations Chatbots, AI-generated content Minimal Risk No regulation Spam filters, game AI Our RAG system's classification: Limited Risk (chatbot). We are required to disclose to users that they are interacting with AI. Directory Structure pgvector-tutorial/ ├── existing files └── governance/ ├── ai_registry.py # ★ AI system inventory ├── risk_assessor.py # ★ Risk assessment ├── audit_logger.py # ★ Audit logging └── compliant_rag.py # ★ Governance-compliant RAG 1. AI System Inventory — governance/ai_registry.py Most organizations lack a systematic inventory of their AI systems, making risk classification and compliance planning difficult. Knowing what you have is the essential first step. # governance/ai_registry.py """ AI system inventory Centrally manage all AI systems in use across the organization. Forms the foundation for technical documentation required by EU AI Act Annex IV. """ from datetime import datetime from dataclasses import dataclass , asdict from enum import Enum class RiskLevel ( Enum ): UNACCEPTABLE = " prohibited " HIG

For years, Zero Trust architectures were designed around one assumption: Humans make the decisions. That assumption is breaking apart. Autonomous AI agents can now query databases, trigger workflows, call APIs, and interact with other systems without direct human involvement. Modern AI systems no longer just generate text. They execute actions inside enterprise environments. When an AI agent can operate on behalf of a user inside your cloud infrastructure, its identity becomes just as critical as any human identity. And that fundamentally changes the security model. The Rise of Tool Calling Platforms like Amazon Bedrock Agents have changed the architecture of enterprise AI. These systems can now interpret a user request, decide which tools are required, and autonomously execute backend operations through Lambda functions, APIs, databases, and external services. A simple prompt can trigger an entire chain of actions. Example Workflow User Prompt: "Summarize customer complaints from the last 30 days." Agent Actions: Query the CRM database Call the analytics API Pull support ticket data Generate a report Powerful for productivity. Extremely dangerous if not properly secured. The New Attack Surface A single successful prompt injection can completely hijack an agent’s behavior. With overly broad permissions, an attacker can force it to: Access sensitive customer data Execute unauthorized API calls Modify records Trigger privileged backend workflows The risk becomes even worse in multi-agent systems. A compromised customer-facing agent can pass malicious instructions to a highly privileged backend agent. Traditional network perimeters and security tools often miss this entirely because the traffic comes from a trusted internal service. Why Traditional Zero Trust Falls Short Classic Zero Trust was designed for human behavior and relatively predictable access patterns. AI agents operate differently: They act autonomously and at machine speed They make decisions without real

A few days ago, Vaibhav Sisinty posted something on X that stopped my scroll: someone had wired up an actual, physical stick shift to switch between Claude models. Not a settings menu. Not a dropdown. A gear shifter, like the one in a car, sitting on a desk. Fable 5 in one gear. Sonnet in another for daily driving. Opus when the problem needs real depth. Slam the stick into position, and the model underneath your workflow changes. The detail that makes this more than a novelty: he built the shifter with Claude, specifically to make his own use of Claude faster. That's a nice little loop — using the model to remove friction from using the model. Why this is a smarter idea than it sounds On the surface it's a gimmick. Under the surface, it's solving a real problem that every heavy AI user runs into: model selection is a decision tax . Every time you open a chat and have to think "is this a Sonnet task or an Opus task?", you're spending attention on meta-work instead of the actual problem. It's a tiny cost, but it's a cost you pay dozens of times a day, and it never shows up on any productivity dashboard. A physical control collapses that decision into a single motor action — the same way a car driver doesn't consciously reason about gear ratios, they just feel the road and shift. That's the actual insight here: the best interface for a decision you make constantly is the one that requires the least conscious thought. A menu makes you look, read, decide, click. A physical lever makes you feel and move. For something you do fifty times a session, that difference compounds fast. A plausible look at how something like this comes together Nobody's published exact wiring diagrams here, but the architecture almost writes itself if you've worked with hobbyist hardware and API-based model switching. Here's roughly what a build like this involves: 1. The physical input layer A repurposed automotive or sim-racing shifter has a set of positions, each one closing a different switc

Every application we use today—from banking apps to social media platforms—has something working behind the scenes. That hidden engine is called the backend. The backend is responsible for processing requests, storing data, handling authentication, enforcing business rules, and ensuring everything works as expected when users interact with an application. One of the first decisions backend developers make is choosing a framework. A framework provides the tools, structure, and best practices needed to build applications faster and more securely. Today, let's look at three popular backend frameworks: Django, Gin, and Ruby on Rails. Django (Python) Django is one of the most mature and feature-rich backend frameworks available. Built using Python, it follows the philosophy of "batteries included." This means many features developers need are already built into the framework, including: User authentication Admin dashboard Database ORM Security protections URL routing Form validation Because so much comes ready to use, developers can spend more time solving business problems instead of rebuilding common features. Best for: Content management systems E-learning platforms Business applications APIs Startups building products quickly Advantages: Fast development Excellent security features Large community Extensive documentation Scales well for many applications Trade-offs: The framework includes many components, so it can feel heavier than minimalist frameworks. Gin (Go) Gin is a lightweight web framework built for the Go programming language. Unlike Django, Gin keeps things minimal. It gives developers speed and flexibility while letting them choose many of the additional tools they want to use. One reason many developers enjoy Gin is its impressive performance. Since Go is a compiled language designed for concurrency, Gin can efficiently handle many requests simultaneously while using relatively few system resources. Best for: REST APIs Microservices High-performance syst

Two hundred and fifty years after the signing of the Declaration of Independence, a new commercial asks: What if the Founding Fathers had access to Google Workspace?

"Forward Deployed Engineer" went from a Palantir-specific title to one of the hottest roles in AI in about eighteen months. But nobody had actually counted the market, so I did. I pulled every open FDE role I could find from public ATS job boards (Greenhouse, Lever, Ashby) across 11 companies and analyzed all 292 of them. Here is what the data says. Who is hiring Three companies account for 250 of the 292 openings: Palantir: 95 (they coined the title, and still call many of these roles "Deployment Strategist") Databricks: 85 OpenAI: 70 Then a long tail: Cohere and Scale AI (13 each), Sierra, Writer, Modal, Baseten, Ramp, and Sardine. What it pays Of the 40 roles that disclosed a US pay band, the median ran $197K to $294K , topping out at $390K plus equity at OpenAI and Sierra, with a floor around $137K. That is senior-software-engineer money for a role a lot of engineers have never heard of. International and most Palantir roles did not publish bands, so the true market is likely even broader. Three things that surprised me 1. 98% of these roles are customer-facing. This is the defining trait. It is not a backend role with occasional meetings. It is an engineer who lives in the customer's world, and if that sounds terrible to you, this is not a role you would enjoy occasionally. It is the whole job. 2. The title is chaos. The same role goes by at least four names: Forward Deployed Engineer (152), Forward Deployed Software Engineer (58), AI or Deployment Engineer (43), and Deployment Strategist (36). If you only search one term, you miss most of the market. 3. The job descriptions undersell the technical bar. JDs emphasize customer-facing work, cloud (AWS/GCP/Azure), Python, and integrations. But SQL and algorithms show up in only about a third of them, even though every FDE loop I have seen tests live coding and SQL under time pressure. The description sells the breadth. The interview tests the depth. The other details Geography: about 48% USA, but genuinely global

The most dangerous moment in an AI-built app project is not when the code breaks. It is earlier. It is the moment where your idea is still blurry, the AI coding tool is sitting there politely, and you type: Build me an app that... That sentence feels productive. It also gives the tool permission to make a pile of decisions you have not made yet. Who is the app for? What is version one? Which workflow matters first? What data has to exist? What should not be built yet? What would make the first version successful? If those answers are missing, AI has to guess. And AI guessing at product shape is how beginners end up with a login system, dashboard, profile editor, notifications panel, admin area, billing flow, and settings page before one real user problem has been solved. That is not momentum. That is software confetti. I like AI coding tools. I use them heavily in real app work. But the tool gets much better when the project has boundaries before code starts changing. So before you ask AI to code your first app, run the idea through a checklist. Not a giant business plan. Not a pitch deck. Not a 47-tab spreadsheet that makes you feel like you joined a corporate strategy retreat by accident. A practical beginner checklist. The goal is simple: turn a rough app idea into something AI can help you build without inventing the whole product for you. 1. Can You Name The Person? Do not start with "users." Start with one person you can picture. Bad: This app is for people who want to be more productive. Better: This app is for freelance designers who need one place to track client feedback, revision status, and final file delivery. Bad: This app is for musicians. Better: This app is for guitarists who want to capture riff ideas quickly on their phone without opening a full mobile studio app. Bad: This app is for students. Better: This app is for college students who want to scan textbook chapters and turn them into study notes before an exam. When you name the person, the ap

▶ Try it live (in your browser): https://umbraaeternaa.github.io/loom/play.html Built solo, in the open, from Ukraine 🇺🇦. The problem nobody can scale their way out of AI now writes a large and growing share of the code that runs in the world. The uncomfortable part isn't that the code is often wrong — it's that the same model frequently writes both the code and the tests that check it. When one intelligence authors the solution and the criteria, "it passed" quietly stops meaning "it's safe." The gate becomes foolable. You can make the model bigger, but a bigger model that grades its own homework is still grading its own homework. The honest answer isn't "trust a smarter model." It's: trust only what can be independently proven — and make that proof mechanical, not a matter of hope. That is the whole idea behind LOOM. What LOOM is LOOM is a small, open-source, effect-typed language that acts as a machine-checked trust layer for AI-written code. It doesn't just run code — it proves, at a gate, exactly what the code is allowed to do, before a single line executes. If the code lies about what it does, the compiler refuses it. The slogan is: AI proposes, the compiler disposes. Today it is a research kernel with 385 self-verifying checks, all green — every feature added only with an adversarial test, so the language can only ever get greener. There's a live browser playground where a stranger can paste a program and watch the checker accept or reject it in under a minute. What it can actually do Effect honesty. Every function declares its effects — Pure, IO, Net, Alloc, FFI, Rand. Declared effects must cover what the code actually does; the lie is caught transitively through calls, branches, recursion — not just straight-line code. Capabilities, not ambient power. A foreign call has no ambient authority — un-wrapped, it's refused. A seam is the only thing that grants authority, so (seam (Pure) (ffi untrusted)) makes that code's I/O physically impossible. Reinterpreting h