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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

Every big social network locks audience data behind auth walls and anti-bot systems. Bluesky went the other way. The AT Protocol is open by design, so public profile data (bios, follower counts, full follower and following lists) is queryable through a documented API without logging in. The whole surface is basically two endpoints: GET https://api.bsky.app/xrpc/app.bsky.actor.getProfile?actor=HANDLE GET https://api.bsky.app/xrpc/app.bsky.graph.getFollowers?actor=HANDLE&limit=100 There's also getProfiles for batching 25 handles per call. Follower lists paginate with a normal cursor , which still works on the graph endpoints. Search is a different story, cursor pagination 403s there now, but that's a topic for another post. For one-off lookups, curl is honestly all you need. Where it gets tedious Bulk. Thousands of profiles, follower exports that run into six figures, weekly snapshots for tracking. Pagination, rate-limit backoff, and stitching the pages together is boring code that has to run reliably. I packaged that part as an Apify actor: Bluesky Profile Scraper . Paste handles or profile URLs, optionally turn on follower/following export, and you get JSON or CSV back with a sourceProfile field linking each follower record to the profile it belongs to. $2 per 1,000 records, runs on a schedule if you want snapshots over time. What people use this for Vetting an influencer's real audience before paying them. Exporting who follows a competitor and what their bios say. Charting follower growth from weekly runs. And enrichment: find who's talking about you with a mentions monitor , then profile those authors to see their actual reach. Bluesky is the only major network right now where any of this is straightforward and stable. Worth using while it lasts.

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

I wanted to know when people mention a brand on Bluesky. Simple ask. Turns out Brandwatch, Mention, Hootsuite, basically every social listening tool, still doesn't cover it. They're all busy with X and Instagram while Bluesky sits at 27M+ monthly users. So I looked at doing it myself and found out something most people miss: you don't need to scrape anything. Bluesky runs on the AT Protocol, which is open by design. Public posts are searchable through a documented endpoint. No login, no API key. GET https://api.bsky.app/xrpc/app.bsky.feed.searchPosts?q=YOUR_BRAND&sort=latest&limit=100 That returns full post objects. Text, author handle, timestamps, like/repost/reply counts, embedded links, hashtags. Everything you need. Two things that broke my first version Worth writing down because most tutorials get this wrong now: The public.api.bsky.app host that older guides point to returns 403 for search. Use api.bsky.app instead. As of July 2026, unauthenticated search rejects cursor pagination. Page one works fine, page two gets you a 403 with "request forbidden by administrative rules". The nasty part is it looks like rate limiting, but it isn't. The workaround: paginate by time. Use sort=latest , then pass until= with the createdAt of the oldest post from the previous page. Dedupe on uri because the boundary post shows up twice. If you don't want to maintain any of that I packaged the whole job as an Apify actor: Bluesky Mentions Scraper . Keywords in, clean JSON out. It handles the pagination and retry stuff above, filters replies if you want, scores basic sentiment, and can pull follower counts for each author so you can sort mentions by reach. Runs on a schedule, exports CSV, plugs into Slack or n8n through Apify's integrations. It also works as an MCP tool inside Claude or Cursor. Pricing is per result, $4 per 1,000 mentions. No subscription. What I actually monitor Brand and product names plus the common misspellings. Competitor names, because share of voice on Blu

I just shipped 5 new tools to DevNestio — a hub of 172 free, browser-only developer utilities. All tools are zero-signup, zero-upload, and work offline. 1. GraphQL Query Formatter & Minifier https://devnestio.pages.dev/graphql-formatter/ Paste any GraphQL operation and get: Pretty-print — consistent indentation Minify — strips comments and whitespace for smaller request payloads Validation — brace/parenthesis balance check Operation detection — lists all named query , mutation , subscription , fragment Useful for quick query cleanup before pasting into code reviews or API docs. 2. Protobuf (.proto) Formatter & Validator https://devnestio.pages.dev/protobuf-formatter/ Online formatter and validator for Protocol Buffer .proto files: Duplicate field number detection Message and enum structure validation Syntax-highlighted output One-click copy Great for a sanity check before pushing .proto changes in a gRPC service. 3. Docker Compose Validator https://devnestio.pages.dev/docker-compose-validator/ Paste your docker-compose.yml to catch: Missing services section Services without image or build Invalid port mappings ( 80:80 , 127.0.0.1:8080:80 , 53:53/udp , ranges…) depends_on referencing non-existent services Circular dependency detection (A→B→A) Unknown restart policies # This will flag errors: services : web : ports : - " abc:xyz" # invalid port depends_on : - missing_service # unknown service 4. Dockerfile Analyzer & Linter https://devnestio.pages.dev/dockerfile-analyzer/ Analyzes your Dockerfile for best practice violations across three categories: Security sudo usage inside RUN Container running as root (no USER instruction) Secrets baked into ENV / ARG (password, secret, token, key) Image size :latest base image tag apt-get update in a separate RUN (stale cache risk) apt-get install without --no-install-recommends apt cache not cleaned ( rm -rf /var/lib/apt/lists/* ) ADD used for local files instead of COPY Layer optimization Consecutive RUN instructions (suggest c

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

TL;DR Two Laravel backends started serving Flutter apps on the same day — an events platform (auth, orders, offline check-in) and a helpdesk product (ops mode for agents). gatherhub-web moved to plans-only pricing with a comparison matrix driven by one data file. A hardening pass: payment-safe queues, gateway reconciliation, one heavyweight dependency dropped. Two mobile APIs in one day Coincidence, but a useful one: two products I'm building both needed their Laravel backends to serve mobile apps this week. The events platform got the full foundation — token auth (login/refresh/logout/me), participant orders, mobile payment with status polling, push-device registration, and an offline-first staff check-in flow. That last one is the interesting bit; I wrote it up as its own post. The helpdesk product went the other way: its API was client-only, and today it became role-aware. The same endpoints now serve ops agents working tickets from their phones, with abilities deciding what each role sees. One API surface, two personas, no duplicated /admin routes. The lesson that repeated in both: API Resources are the contract. The moment a mobile dev consumes your endpoint, every field you accidentally leak becomes a field you can't remove. Plans-only pricing (public) gatherhub-web , the Next.js marketing site, dropped à-la-carte feature pricing for three plans and gained a plan comparison matrix. Everything renders from a single plans.ts — the matrix, the pricing cards, the enterprise page — so the marketing site can't drift from what's actually sold. A pricing page is a contract too; it deserves a single source of truth as much as your API does. Hardening pass Change Why Bulk email blasts isolated to their own queue one big send must never delay a payment webhook Reconciliation command for stuck pending orders webhooks fail silently; polling the gateway is the safety net maatwebsite/excel → spatie/simple-excel for exports streams rows instead of building sheets in memory, s