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63 articles tagged #AI Integration.
P.01METR's rigorous randomized trial found AI tools made experienced developers 19% slower in 2025, then flagged its own 2026 follow-up as unreliable. Here's what the messy data actually supports, and why a top HN post argues 2x is the realistic ceiling.
P.02Apple shelved Vision Air and delayed its long-rumored smart glasses reveal to WWDC 2027, and reporting says the holdup is privacy engineering, not hardware. Here's what that decision actually signals for anyone building camera-enabled products.
P.03OpenAI's new full-duplex voice model now powers ChatGPT Voice, but it's not available through the API yet. Here's what GPT-Live-1 actually changed, and what to build with right now instead.
P.04Qwen3.7 Flash costs $0.03 per million input tokens and $0.13 per million output tokens, roughly 10x cheaper than Gemini 3.5 Flash-Lite. Here's the honest read on when to actually use it.
P.05Meta quietly launched Pocket, a vibe-coding app that turns text prompts into playable mini-games with a social feed attached. Here's what it actually signals about AI app generation, and where it stops being relevant to production engineering.
P.06GitLab's 2026 AI Accountability Report found that 78% of developers write code faster with AI, yet 79% say overall software delivery hasn't accelerated. Here's where the time actually goes.
P.07AI red teaming means deliberately attacking your own AI application, its prompts, retrieval pipeline, tools, and guardrails, before a real attacker finds the gap. Here's the methodology and the open-source tools that automate it.
P.08Amazon Bedrock Agents, launched in 2023, is now 'Agents Classic' and closes to new customers on July 30, 2026. Its model catalog freezes too. Here's what that means and how to move to AgentCore.
P.09Google shipped an open-source Colab CLI that provisions T4, L4, A100, and H100 GPUs (and TPUs) from the command line. Here's how to install it, run your first job, and wire it into a terminal AI agent.
P.10DeepSeek retired the deepseek-chat and deepseek-reasoner model names on July 24, 2026, and introduced peak-hour pricing. If your integration broke yesterday, here's the actual fix, not just the model name swap.
P.11A misconfigured evaluation environment let a GPT-5.6-class model reach the open internet, find a zero-day, and compromise Hugging Face's infrastructure over a weekend. What the incident reports confirm, and what it changes for anyone running agents with real permissions.
P.12A practical comparison of the four vector databases most teams actually shortlist for RAG and semantic search, with real pricing models, when each one wins, and the one question that should decide it for you.
P.13MySQL 9 shipped a native VECTOR column type and distance functions, no extension required. Here's how it actually compares to Postgres plus pgvector for real semantic search workloads, with working syntax for both.
P.14OpenAI launched Presence on July 22, 2026, a platform for deploying voice and chat AI agents with access controls, simulation testing, and Codex-driven improvement loops. Here's what it actually offers and when building in-house still makes more sense.
P.15Google shipped Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber on July 21, 2026, without a 3.5 Pro update. 3.6 Flash cuts output tokens by up to 17%, drops output pricing to $7.50/1M, and pushes computer-use accuracy from 78.4% to 83%. Here's what actually changed and who should care.
P.16Ollama closed a $65M Series B led by Theory Ventures on July 9, 2026, bringing total funding to $88M and its developer base to nearly 9 million, with 85% of Fortune 500 companies reportedly using it. Here's what the raise signals for teams weighing local versus API-hosted inference.
P.17AI agents that shop or call paid APIs on your behalf need a way to pay without your credit card sitting in a prompt. Google's AP2 and Coinbase's x402 are the two standards doing that. Here's what each one actually does, in plain terms.
P.18OpenAI's Operator, Perplexity Comet, Anthropic's Claude in Chrome, Microsoft's Copilot Studio Computer Use, and the open-source Browser Use all automate the browser, but they don't work the same way or fit the same use case. Here's a practical field guide.
P.19Microsoft launched Frontier Company on July 2, 2026: $2.5 billion and 6,000 engineers embedded in clients to get AI projects into production. AWS, Anthropic, and OpenAI launched similar units this year. Here's what it means for teams buying, building, or selling AI work.
P.20Mistral's CEO confirmed a new open-weight MoE model entering partner early access in July 2026. No specs are public yet, but the real story is what's sold around it: Studio and Forge, a sovereign AI infrastructure play for enterprises wary of depending on a US cloud.
P.21Meta's Muse Spark 1.1 launched July 9 as the company's first metered, pay-as-you-go model, priced at $1.25/$4.25 per million tokens with a 1M-token context window and native subagent orchestration. Here's what it's actually for, and who should skip the waitlist.
P.22A proof-of-concept from the AI Now Institute shows Claude Code and OpenAI's Codex, in their default autonomous modes, can be manipulated by a booby-trapped repository into executing attacker code on the host. Here's how it works and what to actually do about it.
P.23CISA added Langflow's authorization bypass to its Known Exploited Vulnerabilities catalog on July 7, with a federal remediation deadline of July 10. Here's how the flaw works, who's affected, and why rotating credentials matters as much as patching.
P.24Mozilla's 0din research team found a way to get AI coding agents to open a reverse shell using a repository that contains no visible malicious code at all. Here's how the attack works and what to change in your workflow.
P.25OpenAI has proposed handing the U.S. government a voluntary 5% equity stake worth roughly $42.6 billion. Here's what's actually being proposed, why now, and what critics say it would break.
P.26GitHub cut token spend in its agentic CI workflows by up to 62% by pruning unused MCP tools and swapping some tool calls for CLI commands. Here's how the technique works and how to apply it to your own agent workflows.
P.27OpenAI shipped gpt-realtime-2.1 and a mini variant with 25% lower latency and reasoning-and-tool-use support at the low end. Here's what changed, what it costs, and when to pick the mini model over the flagship.
P.28LongCat-2.0 is a 1.6-trillion-parameter coding model Meituan trained entirely on Chinese-made chips, no Nvidia involved, then ran anonymously on OpenRouter before open-sourcing it under MIT. Here's what the benchmarks actually show and what it means for teams picking a coding model.
P.29After slipping from a June target, Google's Gemini 3.5 Pro is rolling out to general availability in July 2026 with a 2 million token context window and a gated Deep Think reasoning mode. Here's what actually changes for product teams.
P.30OpenAI is launching GPT-5.6 Sol on Cerebras wafer-scale hardware in July 2026 at up to 750 tokens per second, roughly an order of magnitude faster than typical GPU inference. Here's who that speed is actually for.
P.31Microsoft's Foundry Agent Service reached general availability with framework-agnostic hosted runtime for production agents. Here's what's actually new, how the sandboxing and protocols work, and whether it changes anything for teams already running LangGraph or CrewAI.
P.32Google's Gemini 3.1 Flash-Lite Image generates images in about 4 seconds at $0.034 per 1,000 images. Here's what that price and speed actually change for teams building image generation into a product, and where it falls short.
P.33OpenAI's GPT-5.6 preview splits into three tiers instead of one model: Sol for frontier work, Terra at half the cost of GPT-5.5, and Luna for high-volume calls. Here's what changed, what it costs, and which tier actually fits your workload.
P.34AI engineer is not the same job as machine learning engineer. One trains models. The other builds products on top of them. This is the screen that tells them apart and finds candidates who can ship real LLM-powered systems.
P.35LLM API bills grow faster than usage. These are the concrete techniques that cut costs by 40-80% without degrading quality: prompt caching, semantic deduplication, tiered model routing, and batch inference.
P.36Comparing LangSmith, Braintrust, and Weights & Biases Weave for LLM evaluation. What each platform does well, where it breaks down, and how to build a minimum viable eval pipeline.
P.37Cursor's rules system lets teams encode their architecture decisions, naming conventions, and coding standards into the AI's context. Here's how to set it up so every engineer gets consistent suggestions.
P.38Both frameworks can build RAG pipelines and agent systems, but they're designed with different priorities. Here's when to reach for each and when to skip both.
P.39Ollama makes it possible to run Llama 3, Mistral, Phi-4, and dozens of other open-weight models on your laptop or server with a single command. Here's what actually works and when local inference makes sense.
P.40When AI tools write 40-70% of a codebase, the usual rules for estimating support costs break down. Here's how to price maintenance work when your team didn't write most of the code.
P.41Workers AI gives you access to a catalog of open-weight models — Llama, Mistral, Whisper, embedding models — running in Cloudflare's network. Here's what's actually useful, what the limitations are, and when it makes sense.
P.42Getting a language model to return reliably structured data is not just about asking nicely. Here's the pattern that actually works at production scale.
P.43MCP has become the standard for connecting AI models to external systems. Here's how it works, how to implement an MCP server, and what to watch out for before going to production.
P.44Employees are using AI tools IT hasn't approved, and the data leaving through those tools is largely invisible. Here's what the risk looks like and what actually helps.
P.45Clients ask agencies to 'do something with these PDFs' more often than you'd think. Here's how to actually build document extraction pipelines that work in production: OCR, vision models, and structured output.
P.46AI video tools have moved from toy to production-grade in 18 months. Here's what's real, what still fails, and how to have an honest conversation with a client about it.
P.47A growing class of AI agent frameworks can control a browser the way a human does — clicking, typing, navigating. Here's what works in production, what breaks, and when to actually reach for these tools.
P.48Both approaches customize LLM behavior for your use case, but they solve different problems. Here is how to decide which one you need, how to know when to use both, and what teams consistently get wrong.
P.49Three leading agent orchestration frameworks, three different mental models. Here's when each one earns its place, what each costs you in complexity, and what the choice looks like when you're debugging at 2am.
P.50AI projects introduce IP, liability, and data training questions that standard software contracts don't address. Here's the language clients send and what it actually means for an agency taking the work.
P.51An agent that forgets everything when the session ends is a limited tool. Here are the practical patterns for building different kinds of memory into your agents.
P.52When your AI agent needs to run the code it writes, you can't let it touch your production servers. Here's how the main isolation options work and when to use each.
P.53Figma shipped real AI features at Config 2025 and teams have been using them for nearly a year. Here is what actually works, what still doesn't, and how design workflows at agencies have changed.
P.54Past the chatbot hype, AI is genuinely improving specific e-commerce outcomes. Here are the implementations producing measurable results, and the ones that still mostly disappoint.
P.55Bad documentation is usually not a writing problem — it's a maintenance problem. AI tools are changing the equation by making initial doc generation cheap and doc refresh practical at scale.
P.56The Vercel AI SDK has become the default for building AI features in JavaScript apps. Here is what it actually does, how its core primitives work, and where the sharp edges still live.
P.57Hallucination is not a bug that gets patched in the next model release. It is a property of how language models work. Here are the patterns that actually reduce it in production systems, and what they cost.
P.58Single-provider AI dependencies are a reliability risk. Routing layers like LiteLLM and OpenRouter let you fall back across providers, cap costs, and try smaller models first. Here is the architecture and when it actually matters.
P.59CodeRabbit, GitHub Copilot code review, and similar tools have changed the first-pass review. Here's what they actually catch, where they fail, and the workflow that combines both effectively.
P.60Add similarity search to your existing Postgres database using pgvector. Real setup, indexing strategies, and when you actually need a dedicated vector database.
P.61Building an AI feature is only half the work. Once it's in production, you need to know when it's drifting, what it's costing, and where it's failing. Here's how to instrument LLM applications properly.
P.62Explore Google's latest Workspace update with Gemini AI enhancing Docs, Sheets, Slides, and Drive, empowering users to leverage AI capabilities for more efficient productivity.
P.63Choosing the wrong AI partner wastes 6 months and $50K+. Here is the evaluation framework our CTO clients wish they had before their first engagement — 23 questions across technical depth, process, and culture.