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Building a robust airdrop monitor is no longer just about scraping Twitter or Discord; it requires semantic understanding to filter out noise from genuine, high-value opportunities. Traditional keyword-based bots often trigger on false positives, such as "airdrop" used in a meme context.
Why token prices are only a small part of the cost of running AI in production When an AI feature becomes expensive, the model is an obvious place to look. But as software engineers, we should ask a more uncomfortable question first: how much of the work are we asking the model to do unnecessarily?
I run ModernWebSEO, a small web design and SEO studio in Istanbul. Our own site is in four languages: Turkish (the main market, at the root), English (/en), Spanish (/es) and Arabic (/ar, right to left). The sitemap holds 700 URLs.
I'm a computer engineering student in Manaus, Brazil, and for my research project I've been building ANCHOR, a web automation agent: you describe a task in plain language ("Register Maria Silva, IT department, contractor, accept the terms and save"), and it runs the task in the browser.
What I Built I built AI Nature Explorer, a web application designed to encourage people to explore nature instead of spending all their time on screens. Users can upload a nature photo and use AI to identify what appears in the image.
I recently completed the AI-Assisted Data Science with BigQuery lab via Google Cloud. While analyzing the final multimodal vector search stage, I focused heavily on how the pipeline enforces state dependencies.
AI agents are moving beyond generating code. They can now execute commands, modify files, browse the web, and coordinate with other agents. But here's the question: How much autonomy should we give an AI agent before the security risks outweigh the productivity gains?
"See a feature you like. Understand how it works, down to the binary level." That is the pitch of REA, and it is the one job your coding agent could not do until now.
Most "agents" I ship are a while loop with an LLM call inside. That's fine. The hard part isn't the loop, it's the policy around it: which functions the model is allowed to trigger, which failures justify trying another model, and where the loop stops.
The memory built into a coding agent lives inside that vendor's tools. What I tell one agent stays with that agent, and the next one I open in the same project starts from nothing.
AI agent governance is the set of rules, checks, and controls that decide what an autonomous agent is allowed to do, see, and act on once it's live. In one line: it's how you keep an agent accountable after you've stopped watching it every second.
I built a pipeline that translates the articles I write on Zenn into English and publishes them on dev.to too. I translate locally with Claude Code, and when I push to main, GitHub Actions publishes, updates, or unpublishes the article on dev.to.
OpenClaw 2.0 is not a model release. It is the milestone name for OpenClaw v2026.8.1, which shipped on August 30, 2026, and it converts a mostly single-player agent runtime into a shared, persistent operating layer.
On October 8, 2026, this card came up in an ai i use to Explore feed: Memory & Storage, four stocks, all four down. Micron -4.79%, SanDisk -4.90%, Seagate -4.05%, Western Digital -2.99%. Real order flow across the group: -$1.68B. What each line on the card is for Sector market cap: $1.72T.
One-line summary: By the end of this tutorial, you'll have Kong AI Gateway 2.0 automatically stripping PII from every LLM request using the ai-sanitizer policy. Names, emails and phone numbers get swapped for synthetic values, and your application code doesn't change.
Building an automated airdrop monitor requires more than simple script automation; it demands real-time intelligence to sift through noise on decentralized networks. Traditional cron jobs often miss ephemeral opportunities or fail to assess eligibility criteria dynamically.
Building a Telegram Bot for AI Video Generation: Architecture and Lessons Learned After months of building and iterating on a Telegram-based AI video generation platform, I want to share the technical architecture, challenges encountered, and lessons learned.
Agent skills (the SKILL.md files that Claude Code and Codex load) are easy to share, and GitHub is full of them. Each one was written for its author's project, with their stack, their scripts and their habits. Copy one into your repo unchanged and your agent inherits all of that.
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