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By the end of this page you can say, out loud and in your own words, what every core piece of SQL does. What a table and a row really are. The six clauses, and the order they actually run in, which is not the order you type them. NULL , and why it breaks comparisons. Filtering, aggregation, GROUP BY and HAVING . Joins. CASE . Subqueries, CTEs and window functions. Keys and indexes. That list is most of what an analyst job, an interview, and a first real dataset will ask of you. Here is what to actually do with it. Go through once end to end without stopping, just for the shape. Then come back to the retrieval sheet near the bottom, cover the right-hand column, and try to say each answer before you read it. That second pass is where the learning happens, and there is measured evidence for it further down. The short version: SQL is one sentence with six parts, and every part answers a different question about your rows. Learn what each part does and where it runs, and the rest is vocabulary. One idea decides more of your SQL experience than any other, so it gets the picture. You write a query in one order. The database runs it in a different order. Almost every confusing SQL error is that gap. The original carries a diagram here. In words: Two columns of stacked boxes face each other. The left column, headed "you write", lists the clauses in typing order from top to bottom: SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, LIMIT. The right column, headed "it runs", lists the same clauses in execution order: FROM, WHERE, GROUP BY, HAVING, SELECT, ORDER BY, LIMIT. Curved lines connect each clause on the left to the same clause on the right. Six of the seven lines run roughly straight across. One line, the one belonging to SELECT, is drawn in a strong accent color and sweeps steeply downward from the very top of the left column to the fifth position on the right, showing that SELECT is written first but runs almost last, after grouping has already happened. What this page

Automating Your Morning: A Daily Briefing Pipeline You Can Build You should not manually read news, emails, or Slack in the morning. The average knowledge worker loses 23 minutes to context switching between 8:00 AM and 9:30 AM, according to a 2023 RescueTime study. That is 92 hours per year—two full workweeks—spent on low-signal input. The fix is not "waking up earlier." The fix is building a passive briefing pipeline that compiles, ranks, and summarizes your information sources before you open your laptop. This article shows you the exact architecture, tools, and failure points, based on my own production setup running for 14 months. The Problem: Your Morning Input Is Unstructured Here is the chain of causality. You wake up and check three things: email, Slack/Teams, and newsfeeds. Each app is a separate silo with its own notification system. Each notification triggers a micro-decision: Is this urgent? Do I need to act? Should I forward this? That decision process is not free. A 2022 University of California Irvine study measured that after each interruption, it takes an average of 23 minutes to return to deep focus. But most people never return to deep focus in the morning—they just bounce between silos. The result is "reactive paralysis": you start your day by responding to others' priorities, not your own. And because each silo sorts by recency (not importance), you read a promotional email from your bank before a critical client update. Why Manual Curation Fails You might think, "I'll just spend 10 minutes skimming." Let me give you the math. If you receive 50 emails, 30 Slack messages, and 20 industry news headlines, that is 100 items. At 6 seconds each to decide relevance (not read), that is 10 minutes of pure triage. But you will read the interesting ones—that is a minimum of 45 minutes total. The deeper issue is recency bias . News apps show you the latest story, not the most important one. Email shows the newest sender, not the highest-value contact. With

This guide gives you a test that takes ten seconds and tells you whether the thing you just built is a report or an analysis. Then it gives you four moves that turn one into the other. Every move has a worked SQL example and real numbers. The whole method is here. What you actually do: take the number you just produced, and ask what someone would do differently because of it. If the honest answer is nothing, you have a report. Then you run the four moves below, in order, until the answer is a specific action a specific person can take on Monday. The short version. Data analysis is looking at records of things that already happened and finding a pattern that changes what someone does next. If nothing changes, it was not analysis. It was a report. The same starting number, two endings. The test: what would someone do differently? Before you read the answer, look at the last thing you built and try it yourself. Who was going to act on it, and what were they going to do? Take any number you have produced and finish this sentence out loud: "Because of this, someone should do a specific thing ." Both blanks have to fill in with something real. A named person or team, and an action they control. Here is a real one. "Churn was 4.1% in Q3." Who acts, and how? Nobody can act on that. It is a true, correctly calculated, carefully formatted number, and it changes nothing. That is a report, and reports are useful. A dashboard that tells you the servers are up is doing its job. It is just not analysis. Now the same underlying data, worked further. "Monthly-plan accounts that never opened the import tool churn at 9.2%. Ones that did churn at 1.8%. The email introducing that tool goes out on day 14, and most cancellations happen on day 11." Who acts? The lifecycle marketing owner. What do they do? Move the email to day 3. That is analysis, and the only difference is that it ended somewhere a person can stand. The word "analysis" is doing a lot of quiet work in job descriptions, so

In a recent study, evolution gets even messier than usual.

Security researchers found that in less than 60 seconds, they could open a hatch on a plane’s exterior, plug in a tiny device, and redirect the aircraft’s autopilot or sabotage its flight plan.

In this episode of the podcast, members of the InfoQ editorial staff and friends of InfoQ will discuss current trends in the cloud and DevOps domains as part of our annual trends report. These reports provide InfoQ readers with a high-level overview of key topics to watch. This podcast offers a chance to hear our raw conversation and the stories shared by our expert practitioners. By Daniel Bryant, Matt Saunders, Shweta Vohra, Steef-Jan Wiggers, Mark Silvester

Last week, an AI newsroom beat mainstream journalists—including WIRED—to a story about OpenAI and hacking. It’s just the beginning.

From pessimism around dating to AI reshaping culture, cyber-ethnographer Ruby J. Thelot tells WIRED why people are putting too much stock into things that go viral.

I spend a lot of time in the AI space -- reading papers, building things, talking to engineers who are actually shipping. And there is a gap between what the demos show and what production systems actually look like that nobody is being fully honest about. So here is my honest take on where things actually are. The Problem With How We Talk About AI Agents Everyone is calling everything an "agent" right now. A function that calls a tool? Agent. A chatbot with memory? Agent. A script with a loop? Agent. This dilution is not just semantic. It is causing real engineering mistakes. When you do not have a precise definition for what you are building, you end up over-engineering simple pipelines and under-engineering genuinely complex ones. I have seen teams spend weeks adding "agentic" orchestration to workflows that would have been fine as a single well-structured prompt. Here is the definition I keep coming back to: an agent is a system that has an objective, not just an instruction. It decides what to do next. It handles failure. It knows when it is done. Everything else is just a fancy function call. 🟢 If your system needs a human to tell it each step, it is not an agent. It is a chat interface. 🔵 If your system can recover from a failed tool call and try a different approach, you are getting somewhere. ✅ If your system can decompose a goal into subtasks and delegate them, that is the real thing. What Is Actually Happening in Production Right Now The honest picture from teams I follow and talk to: Most real agent deployments are narrow. They do one thing well. Customer support triage. Document extraction. Code review on a specific codebase. They are not general-purpose reasoning engines. They are purpose-built pipelines with some intelligence in the decision layer. The teams getting good results are not chasing the latest model release. They are obsessing over: ☑️ Tool design -- what can the agent actually call, and how clean is the interface ☑️ Failure handling -- wh

I spend a lot of time in the AI space -- reading papers, building things, talking to engineers who are actually shipping. And there is a gap between what the demos show and what production systems actually look like that nobody is being fully honest about. So here is my honest take on where things actually are. The Problem With How We Talk About AI Agents Everyone is calling everything an "agent" right now. A function that calls a tool? Agent. A chatbot with memory? Agent. A script with a loop? Agent. This dilution is not just semantic. It is causing real engineering mistakes. When you do not have a precise definition for what you are building, you end up over-engineering simple pipelines and under-engineering genuinely complex ones. I have seen teams spend weeks adding "agentic" orchestration to workflows that would have been fine as a single well-structured prompt. Here is the definition I keep coming back to: an agent is a system that has an objective, not just an instruction. It decides what to do next. It handles failure. It knows when it is done. Everything else is just a fancy function call. 🟢 If your system needs a human to tell it each step, it is not an agent. It is a chat interface. 🔵 If your system can recover from a failed tool call and try a different approach, you are getting somewhere. ✅ If your system can decompose a goal into subtasks and delegate them, that is the real thing. What Is Actually Happening in Production Right Now The honest picture from teams I follow and talk to: Most real agent deployments are narrow. They do one thing well. Customer support triage. Document extraction. Code review on a specific codebase. They are not general-purpose reasoning engines. They are purpose-built pipelines with some intelligence in the decision layer. The teams getting good results are not chasing the latest model release. They are obsessing over: ☑️ Tool design -- what can the agent actually call, and how clean is the interface ☑️ Failure handling -- wh

The problem AI agents like Claude Code and Codex write code fast. You run it, it works, you merge. A week later, there's a bug — and you realize you never actually understood the code you shipped. You just transcribed it. This is "vibe coding," and it's becoming the default way a lot of us write software now. What I built BuildIt is a set of hands-on courses where an AI agent proposes code changes like a normal diff — but you can't move to the next step until you explain, in an actual conversation with an AI tutor, why the change was made and what could go wrong. You also write the prompt yourself before the AI generates anything. No skipping. No checkbox you can fake. Real, compilable code from lesson one — not toy examples. 9 courses, 45 real shipped projects: Arduino STM32 (HAL) STM32 (LL) ESP32 Next.js Python React React Native Flutter How it works An AI agent proposes code (same diff screen you already know from Claude Code, Codex, Antigravity) BuildIt demands a line-by-line explanation before you can approve it An AI tutor verifies your understanding through real conversation Only then do you move to the next step Technical details The tutor AI runs entirely locally in your browser — your code never leaves your machine Credits-based pricing — unlock a course, it's yours even if you cancel later Built for teams too — share credits across an org, instill review habits from day one Why this matters AI will write more of our code over time, not less. That makes the ability to actually read and verify it more valuable, not less. BuildIt isn't trying to teach you to write code from scratch — it's trying to make sure you don't lose control of the code an AI writes for you. Would love feedback from anyone who's felt that "I merged this AI diff and don't actually understand it" moment. Try it here

Phia, the shopping startup co-founded by Phoebe Gates and Sophia Kianni, is once again under fire for its alleged business practices.

The app was banned from government devices in 2022.

But will Gemini's surge survive slowing model releases?

Part 7 (final) of a series building a support-ticket agent with no framework. Previous: Part 6 (observability). Repo: github.com/akash-pal/agent-from-scratch The other six parts described the finished design. This one is about what "finished" actually took — the real bugs the eval set caught, and the two questions every agent build eventually has to answer honestly: do you need more than one agent, and do you need a framework. Full detail on everything below: docs/iteration-log.md . The iteration log, condensed 1. Exact trajectory matching was the wrong check. First eval run: 12/21 passed. Most failures were the agent correctly sending a confirmation email where the eval only expected a lookup — correct behavior, wrong assertion. Fix: switched the harness from exact-array equality to ordered-subsequence matching (every expected tool must appear, in order; extra steps in between are fine). Still catches a missing, reordered, or wrong tool. Stops false-failing on benign non-determinism. 2. No retry/backoff meant a transient error crashed the whole run. A 503 — model overloaded on case 1 took down the entire eval harness. Fixed with exponential backoff on 429 / 503 specifically, plus inter-case pacing to stay under free-tier rate limits. 3. A -latest model alias silently rolled onto a stricter quota. gemini-flash-latest worked, then started failing with a 20 requests/day cap after quietly resolving to a newer model. Fixed by pinning an explicit model version instead of an alias, after checking the provider's live usage dashboard for actual quota — 25x more headroom on the pinned model. The takeaway generalizes past this one provider: "latest" aliases optimize for capability, not quota stability, and what they resolve to changes over time without your code changing at all. 4. A testing artifact that looked like a real bug. Piping multiple answers into the interactive CLI via printf "a\nb\n" | npm run agent intermittently hung after the first prompt. Root cause: a Node.j

Part 4 of a series building a support-ticket agent with no framework. Previous: Part 3 (the eval set). Repo: github.com/akash-pal/agent-from-scratch This is the part everyone reaches for a framework to skip. Here's the argument for not doing that, at least the first time: if you can't explain what your agent loop does in plain English, no framework is going to fix that — it's just going to make the loop harder to see. Here's src/agent.ts , trimmed to the actual loop: export async function runAgent ( ticket : Ticket , customer : Customer | null , approvalFn : ApprovalFn , maxSteps = 8 , ): Promise < AgentResult > { const state = initState ( ticket , customer ); // Guardrail check happens BEFORE any model call — see Part 5. const escalatePattern = matchAutoEscalate ( ` ${ ticket . subject } ${ ticket . body } ` ); if ( escalatePattern ) { return { outcome : " escalated " , finalText : `ESCALATED: auto-escalated — " ${ escalatePattern } "` , state }; } const ai = new GoogleGenAI ({ apiKey : process . env . GEMINI_API_KEY }); const contents : Content [] = [{ role : " user " , parts : [{ text : ticketToUserMessage ( ticket ) }] }]; for ( let step = 0 ; step < maxSteps ; step ++ ) { const response = await withRetry (() => ai . models . generateContent ({ model : MODEL , contents , config : { systemInstruction : buildSystemPrompt ( COMPANY ), tools : [{ functionDeclarations }] }, }), ); const calls = response . functionCalls ?? []; if ( calls . length === 0 ) { // No tool call — the model produced a final answer. Done. const text = ( response . text ?? "" ). trim (); return { ... enforceOutcomeIntegrity ( parseOutcome ( text ), state ), state }; } // Otherwise: execute the requested tool(s), feed results back, loop again. contents . push ({ role : " model " , parts : response . candidates ?.[ 0 ]?. content ?. parts ?? [] }); const responseParts = []; for ( const call of calls ) { const result = await executeToolWithGuardrails ( call , state , approvalFn ); // Part 5 respon

Part 2 of a series building a support-ticket agent with no framework. Part 1 covered why. This part covers Steps 1–2 of the build order: pinning the use case, and writing tool contracts. Repo: github.com/akash-pal/agent-from-scratch Before any code, two documents: docs/use-case.md and docs/tool-contracts.md . Skipping this step is the single most common reason teams end up with an agent nobody trusts — not because the idea was bad, but because nothing downstream (evals, prompts, memory) had a fixed target to hit. Step 1: pin the use case Four gates, filled in before writing a line of code: Gate Definition Bounded input One support ticket: { subject, body, customer_id, order_id? } Bounded output Exactly one of: resolved , refund_proposed (pending approval), escalated (with a reason) Tool count 5 Success metric Resolution rate > 85% without escalation; escalation rate < 10% The tool count cap matters more than it looks. An agent given 10+ tools starts hallucinating tool names and picking the wrong one — a cognitive load problem, not a dependency problem. Keeping this agent to 5 tools, covering exactly three request types (order status, refunds, KB lookups), keeps every run in the healthy 3–8 tool-call range instead of ballooning into a system that needs to be split into multiple specialist agents. (Part 7 covers the actual cost math for when a split is worth it.) Bounded output matters too: resolved / refund_proposed / escalated isn't just documentation — it becomes a literal parseable prefix ( RESOLVED: , REFUND_PROPOSED: , ESCALATED: ) that the agent's final message must start with. Part 4 shows exactly how that gets parsed, and Part 5 shows why trusting that string alone turned out to be a real bug. Step 2: tool contracts are a schema, not a docstring This is the part that's easy to under-invest in. A tool's description field isn't a comment for future developers — it's the only thing the LLM reads to decide when to call the tool. Treat it as a specification. Here'

Thirteen authors joined a little-known Google AI study in 2022. Now they're facing backlash.

Over a year following its post-election surge, Bluesky’s mobile app is seeing a continued decline in active users, though its remaining community is still relatively engaged.

Joby Aviation reached an agreement to acquire Resonant Sciences, which will kick off its new defense business.