标签：#AR

A platform is a collaboration system: platform teams depend on application teams, and both need shared standards. Engineers trust a platform through its predictable behavior, not its features. Being an engineer is about problem-solving and being passionate about it. And being an engineer means sharing your passion for problem-solving. By Ben Linders

The Dell 14S represents the new normal of laptop pricing, but it has the quality to back up its cost.

The Amseatec Criss Cross Office Chair gives you room to sit cross-legged, sideways, or however your body actually wants to sit.

With pricing in flux and new models available, here’s which Surface Laptop and Surface Pro to buy.

NHTSA administrator Jonathan Morris called reports that self-driving cars had driven into emergency scenes and blocked ambulances and firefighters “unacceptable.”

AI founders love the glamorous agent stories: coding agents, sales agents, AI doctors, AI lawyers. But if you dig through the YC 2026 batch data, one of the more interesting signals is decidedly unglamorous: insurance . Out of 477 real-ish company records in the current snapshot, 25 match insurance-related keywords — about 5.2% — and 8 companies sit in the Fintech → Insurance subindustry. Not a tidal wave. But it's enough to suggest something worth paying attention to: insurance is quietly becoming one of the better wedges for AI agents that actually ship. The reason is simple. Insurance is wall-to-wall documents, rules, judgment calls, exceptions, approvals, claims, underwriting, and cross-system coordination. In other words: wall-to-wall work that agents can do and humans hate doing. Insurance is not fintech's leftover category Most people file insurance under "slow fintech": aging distribution, legacy systems, long processes, heavy regulation. From an AI builder's perspective, that list of flaws reads more like a list of opportunities. Insurance workflows are highly structured — but not fully structured. Policies, claims files, medical records, photos, repair estimates, payout history, compliance clauses: the inputs are messy and heterogeneous. Yet every step has a crisp objective: is this covered, what documents are missing, how should this risk be priced, can this pass approval. That's not a chatbot problem. It's an agent problem — reading documents, following procedures, calling systems, leaving audit trails, handling exceptions. And precisely because it's complex, insurance is more likely to command real budget than yet another AI writing tool. Agents die without boundaries; insurance comes with them built in The most common failure mode for early agent products: they sound like they can do everything and end up doing nothing well. Insurance workflows hand you boundaries for free: Inventory and asset processes can be automated end to end Medical prior authori

If you could pick only one counterintuitive number from the YC 2026 batches, make it this one: out of 477 real-ish company records, 366 list San Francisco as their location — roughly 77%. For comparison: New York City has 24. London 10. Boston 7. Los Angeles 4. Fully remote? 3 companies. Even if you add the 11 tagged "San Francisco + Remote", the conclusion doesn't budge: AI startups aren't spreading across the map. They're re-concentrating in one city. This isn't Bay Area nostalgia. It's industry structure casting a vote. Remote won work. It didn't win startup density. One of the most popular takes of the past few years: software teams can start anywhere, so companies no longer need the Bay Area. That take wasn't entirely wrong — tooling, cloud services, open models, and online fundraising genuinely lowered the barrier to starting a company. But the YC 2026 location data is a reminder that a lower barrier is not the same as a vanished advantage. Building an AI startup isn't just writing code. It runs on model gossip, talent flow, customer pilots, investor feedback, peer pressure, and extremely fast narrative iteration. Much of that works online. But the densest informal information still travels fastest offline. San Francisco's edge was never the office space — it's collision frequency. AI made same-city learning matter again In the classic SaaS era, most domain knowledge came from customers and product cycles were relatively stable. You could build a vertical software company in any city and grind toward PMF at your own pace. The AI era doesn't work like that. Model capabilities turn over every few months. Agent architectures keep getting rewritten. Inference costs, context windows, voice, tool calling, and eval infrastructure are all on rolling release. A seemingly minor technical shift can redraw your product's boundaries overnight. In that environment, whoever hears real feedback earlier, learns earlier what others tripped over, and understands earlier what inv

For a long time, education and work rewarded one thing above all else: the ability to produce correct answers. School exams were built around it. Technical interviews were built around it. Even many engineering jobs were built around it. The person who could respond faster, explain better, and deliver the right output was often seen as the most valuable person in the room. But AI is changing that. Today, answers are becoming cheap. With modern AI tools, anyone can generate code, summaries, documentation, architecture drafts, and even product ideas in seconds. The scarcity is no longer in producing answers. The scarcity is in defining the right problem. That is why, in the AI era, learning how to ask better questions matters more than learning how to write better answers. The Bottleneck Has Moved The biggest shift is not that AI can answer questions. The bigger shift is that answering is no longer the hardest part. When answers can be generated instantly, the real bottleneck becomes: What exactly should be asked? What is the real problem behind the surface request? What constraints actually matter? What outcome is considered good enough? AI can generate many possible answers. But it still depends heavily on the quality of the question. A vague prompt creates vague output. A precise question creates leverage. In that sense, the person who defines the problem is now more important than the person who simply responds to it. The Problem Setter Is More Valuable Than the Problem Solver This idea may sound exaggerated at first, but it becomes obvious in practice. Suppose someone says: Optimize this system. That sounds like a reasonable task, but it is actually too weak to produce a strong result. Optimize for what? Cost? Latency? Reliability? Simplicity? Team productivity? Now compare it with this: We have a Node.js API running on AWS ECS. Under burst traffic, CPU throttling causes latency spikes. How can we reduce p95 latency without increasing infrastructure cost by more

We just opened the waitlist for Something, and the part that surprised me most while building it wasn't the multi-agent orchestration — it was how hard it is to make an AI actually disagree. Every model we tested defaults to being helpful, which in practice means agreeable. Even when explicitly prompted to "find flaws," the outputs would soften into "here are some considerations" instead of a real critique. We had to engineer around this specifically: Separate system prompts with opposing reward framing — one agent optimizes for identifying growth potential, the other is explicitly told its only success metric is surfacing a disqualifying flaw Structured output forcing a verdict, not a summary — the skeptic agent (Nothing) has to commit to a specific weakness category (unit economics, timing, technical feasibility) rather than hedging across all of them A reconciliation step where both outputs get merged into one conviction score, so the founder isn't just reading two contradictory paragraphs If anyone's built adversarial agent setups and hit the same "it just wants to agree with me" problem, curious how you solved it. [Everyone who has a brain is a founder here] something-waitlist.vercel.app

I believe Angular upgrades have become much smoother these days. Most of the time, a simple ng update is enough to move to the latest version. Instead, I spent hours chasing errors that looked completely unrelated to the real problem 😭 After upgrading the project to Angular 21, I started seeing errors like these: Cannot find module '@angular/material/chips' Cannot find module '@angular/material/dialog' Then another one appeared: Error: The current version of "@angular/build" supports Angular ^19... but detected Angular version 21.x instead. At first, it looked like Angular Material wasn't installed correctly but i think the actual issue was a version mismatch inside the project. Some packages had already been upgraded to Angular 21: @angular/core @angular/common @angular/material But the build system was still using: @angular-devkit/build-angular@19 Since Angular's build tools are tightly coupled with the framework version, the compiler started producing misleading errors. The build pipeline was the problem. The Commands That Helped I used these commands: npm ls @angular-devkit/build-angular npm explain @angular-devkit/build-angular They showed that my project was still resolving Angular 19's build package. That was the clue I needed and than I verified that every Angular package was using the same major version. Then I cleaned the project completely: rm -rf node_modules rm package-lock.json npm cache clean --force npm install It takes time usually.(and I did it several times cause Im failed 😃) Finally, I confirmed that all Angular packages were aligned before building again.

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tags: hardware, iot, opensource, electronics As software developers, many of us reach a point where writing code inside a virtual environment isn't quite enough—we want to manipulate the physical world. Whether it's blinking an LED via an ESP32, visualizing audio frequencies on a desk display, or building custom bench tools, hardware hacking is easily one of the most rewarding rabbit holes to fall down. At NextPCB , we’ve spent the past few years supporting the open-source hardware community by sponsoring independent creators, makers, and embedded engineers to help turn their digital schematics into real, physical circuit boards. If you’re looking for inspiration for your next weekend project, here are four curated roundups of real-world projects featuring open-source files, schematics, and design breakdowns. 1. Retro Tech & Nostalgic Geek Culture Builds 🎮 There’s something uniquely satisfying about recreating classic tech using modern hardware components. From custom hand-held arcade consoles to retro synth modules and glowing mechanical displays, retro builds combine aesthetic nostalgia with serious embedded engineering. These projects aren't just for show—they showcase clever power management, compact multi-layer PCB routing, and custom display interfaces. 👉 Check out the project breakdowns & schematics: 8 Retro Geek Culture PCB Projects: Open-Source Gerbers & Schematics 2. Smart Audio & Interactive Visual Displays 🎵 Audio reactive electronics bridge the gap between digital signal processing (DSP) and hardware UI/UX. Think custom spectrum analyzers, RGB LED matrix drivers, and tactile smart knobs that update in real-time. Building custom audio hardware requires paying extra attention to noise isolation, clean power delivery, and signal integrity—making these projects fantastic learning material for intermediate hardware devs. 👉 Explore the audio & display designs: Smart Audio & Interactive Display PCBs: Open-Source Design Guide 3. DIY Power & Precision Lab Equipm

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Google shipped AlloyDB AI functions GA with a proxy model architecture that trains a lightweight local model from LLM outputs, then runs queries at database speed without external calls. Smart batching delivers 2,400x throughput improvement. The proxy model reaches 100,000 rows per second in preview, but benchmark numbers apply only to ai.if in internal testing. By Steef-Jan Wiggers

I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power. Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can…

The Kubernetes community has introduced a framework for integrating AI into open-source maintainership, emphasising human accountability in code quality and oversight. AI tools may streamline workflows, but ultimate responsibility lies with human maintainers. The framework requires disclosure of AI usage in contributions and prohibits AI-generated commit messages. By Olimpiu Pop

Some of you may have noticed I disappeared a bit from the community over the last couple of weeks....

Eleven exhibits, last time. A career that kept refusing to fit inside a single box — trainer,...

I have been building software for sixteen years. I have four ambassador titles I earned honestly. And last week I sat at my desk at eleven at night, certain that everyone else my age was further ahead than me. You know that feeling. The one where you scroll past someone's launch, someone's promotion, someone's clean little success, and a cold voice says you should be there by now. It does not care what you have done. It only points at what you have not. For most of my career I treated that voice as a problem to solve. If I could learn one more tool, ship one more thing, earn one more title, it would finally go quiet. So I did. I learned the tools. I shipped the things. I earned the titles. The voice did not go quiet. It moved the finish line and waited for me there. Here is the opinion I wish someone had handed me a decade ago. Feeling behind is not a bug in you. It is the tax you pay for caring about the work. The people who feel the most behind are almost never the ones who are actually behind. They are the ones paying attention. They see the gap between what they made and what they meant to make, and that gap never closes, because the moment you get better, your taste gets better too. The gap is not evidence that you are failing. The gap is proof that you still have standards. I know engineers with twenty years and a wall of real accomplishments who quietly feel like frauds. I know brilliant people five years in, staring at a job market that feels brutal, convinced everyone else got a memo they missed. None of them are behind. All of them are exhausted from running a race that has no finish line, on a track only they can see. The comparison is rigged, and it is worth saying why. You compare your inside to everyone else's outside. You know your own doubt, your own half-finished drafts, your own two in the morning. You see their launch, their title, their highlight. You are matching your bloopers against their trailer, and then calling yourself slow. So what change

Originally published on tamiz.pro . The Signal: Core Developer Competencies Effective code evaluations must identify signal - the skills that directly impact software quality and long-term maintainability. Focus on: Problem-Solving Approach : How candidates break down complex problems Code Structure : Organization, modularity, and separation of concerns Edge Case Handling : Proactive identification of boundary conditions Test Coverage : Implementation of meaningful unit/integration tests Performance Awareness : Appropriate algorithm selection and resource management These elements predict real-world engineering capabilities, not just syntax mastery. The Noise: Common Evaluation Pitfalls Avoid overemphasizing noise - factors that correlate weakly with actual job performance: Noise Factor Why It Fails Signal Alternative Coding style Reflects personal preference Consistency within project conventions Syntax errors Easily fixed with linters Code correctness after tooling Solution speed Varies by individual Final solution quality Language trivia Library/framework knowledge changes Core programming principles Interview anxiety Doesn't reflect daily work Paired programming sessions Measuring Signal Effectively Task Design : Create realistic coding challenges that mirror production problems Rubric-Based Evaluation : Use weighted scoring matrices focused on signal factors Code Review Simulations : Evaluate candidates' ability to interpret and improve existing codebases Collaboration Metrics : Track communication clarity during pair programming sessions Iterative Development : Assess how well candidates refine solutions based on feedback Signal Amplification Techniques Time-Bounded Challenges : Set strict time limits to reduce focus on perfectionism Tooling Freedom : Allow candidates to use their preferred IDEs and debugging tools Post-Coding Debrief : Ask candidates to explain their design choices and tradeoffs Follow-Up Questions : Test understanding of implementation decis