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Even after your movies end, these art televisions look stunning on any wall.

Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing the…

Apple’s fall macOS release will let you build Shortcuts by typing what you want to happen. But Claude Code and Codex users don’t have to wait.

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Single-model systems are simple. Multi-model systems are powerful. The challenge isn't choosing models — it's designing the architecture that orchestrates them. A multi-model system isn't about having more models. It's about having the right model for the right task at the right time. Architecture patterns Five patterns cover most use cases: Pattern Complexity When to use Tradeoff Single Model Lowest Prototyping, simple tasks Limited capability Sequential Low Multi-step workflows Higher latency Parallel Medium Independent tasks Higher cost Hierarchical High Complex reasoning Complex orchestration Ensemble Highest Critical decisions Highest cost Pick the simplest one that works. Complexity is real, and it compounds. Sequential architecture Process tasks through a chain of models, each specializing in a step. Pattern 1: Pipeline Pipeline pattern — each model's output feeds the next: class ModelPipeline : def __init__ ( self ): self . models = [ { " model " : " qwen2.5-1.5b " , " task " : " classify " }, { " model " : " qwen2.5-7b " , " task " : " extract " }, { " model " : " qwen2.5-32b " , " task " : " reason " }, ] def process ( self , input : str ) -> str : current = input for model_config in self . models : current = self . call_model ( model_config [ " model " ], self . create_prompt ( model_config [ " task " ], current ) ) return current Latency adds up. Three models in sequence means three times the latency. Only use this when each step actually needs a different model. Pattern 2: Router Router pattern — classify the task, route to the specialist: class ModelRouter : def __init__ ( self ): self . classifier = " qwen2.5-1.5b " self . specialists = { " code " : " qwen2.5-coder-7b " , " math " : " qwen2.5-32b " , " creative " : " claude-sonnet-4 " , " general " : " qwen2.5-7b " , } def route ( self , prompt : str ) -> str : task_type = self . classify ( prompt ) model = self . specialists . get ( task_type , self . specialists [ " general " ]) return self . call_m

There is a comfortable lie that has taken root in information security domain. It goes like this: "We have invested in the best tools. We have CSPM. We have CNAPP. We have ASM, ASPM, EDR, SIEM. We are covered." Boards believe it. CISOs present it. Security budgets are built around it. And while everyone is looking at dashboards full of green, two things are quietly true: One — attackers are not trying to beat your tools. They are looking for what your tools were never designed to see. Two — the most dangerous gaps in your security posture today are not gaps in your tooling budget. They are gaps in visibility, knowledge, and the fundamental understanding of what your own systems are supposed to do — and whether they actually do it. No tool solves that. Only people do. The Tool Trap The security industry has industrialized the idea that protection is a purchasing decision. Spend enough. Deploy enough. Integrate enough. And you will be secure. This thinking has produced something genuinely useful — a generation of powerful tools that automate detection, surface known misconfigurations, and reduce the manual burden on security teams. CSPM catches publicly exposed storage buckets. EDR detects malware. SIEM correlates suspicious behavior across logs. These tools matter and they work — within the boundaries of what they were designed to see. But there is a boundary. And most organizations have no idea where it is. The boundary is this: every security tool operates on known patterns, defined rules, and observable configuration state. None of them operate on context. None of them understand intent. And intent — what a system was supposed to do, how it was supposed to be accessed, what data it was never supposed to expose — is exactly where the most dangerous vulnerabilities live today. The Dangerous Gap Nobody Has Named Yet Security practitioners are familiar with the concept of IoM — Indicator of Misconfiguration. A wrong setting. An overpermissioned role. A flag that flipp

PSYONIC's prosthetic touch data is now training ABB robots. Gatik signed the first Fortune 50 commercial autonomous freight contract with PepsiCo. Burro drove Physical AI onto the construction site. Experts set $20k as the humanoid price target. And someone just called Edge AI the Windows of robotics. This week, Physical AI crossed three invisible lines at once. A company that makes prosthetic hands figured out that the touch data from amputees is exactly what industrial robots need to learn how to grip. A Fortune 50 company signed not a pilot but a commercial contract for autonomous freight. A 44-horsepower robot drove off the warehouse floor and onto the construction site. And two separate conversations about software and pricing suggest that the next wave of robotics adoption will be driven by access, not capability. Here is what happened, and why it matters beyond the headlines. Value Description Fortune 50 PepsiCo becomes first to sign a commercial contract for autonomous freight with Gatik $20k Target price point for humanoid robots, Robotics Summit consensus: achievable by 2028–2030 1M hours Burro's field experience backing the Grande 44 autonomous outdoor platform 100+ Pressure sensors per fingertip in PSYONIC's Ability Hand, now training ABB GoFa A Prosthetic Hand Is Now Teaching an Industrial Robot How to Grip The standard approach to teaching a robot how to handle objects has been simulation, teleoperation, or labor-intensive physical demonstrations. PSYONIC and ABB just introduced a different source of data : the hands of people who have already learned to feel again. PSYONIC's Ability Hand is a prosthetic with more than 100 pressure sensors per fingertip . The company has been collecting kinesthetic data from users with upper-limb amputations. That data, which captures how a human hand adjusts grip pressure, contact area, and force across thousands of everyday tasks, is now being fed as training data into ABB GoFa robot arm models. The implication is no

How I Cut My Multimodal AI Costs by 97% — A Freelancer's Guide Last month I almost killed a side gig because of a single line item on an invoice. A client wanted me to build a document-processing tool that could read scanned PDFs, pull text out of photos, and answer questions about charts. Easy enough — except I'd quoted the job assuming I'd use GPT-4o for the vision work. When I actually ran the numbers, I realized the API bill would eat my entire margin. I'd be working for free. Maybe worse. So I did what every freelancer does when the big-name vendor gets too expensive: I went hunting. And I landed on Global API, which routes to a bunch of multimodal models I've honestly never heard clients talk about. After a few weeks of testing, I figured out which ones are worth my billable hours and which ones aren't. This is everything I learned, plus the exact code I'm shipping to clients. Why Multimodal Even Matters for Solo Devs Two years ago, "multimodal" was a buzzword you'd hear at conferences. In 2026 it's table stakes. I've personally used vision models to: OCR receipts for an expense-tracking app (boring but pays the rent) Convert screenshots of legacy code into editable source for a Y2K-era company migration Read bar charts from PDF reports for a finance client who hates spreadsheets Analyze medical imaging samples for a startup MVP (this one was scary) Every one of those jobs started as a quick conversation with a prospect and turned into real invoices because I could say yes. The bottleneck was never capability — it was always cost. When GPT-4o charges north of $10/M output tokens, a single 2,000-token response on a tricky chart costs me about two cents. Multiply by 10,000 images per month and you've got a $200 API line item before you've paid yourself. That's a problem when the whole job is worth $400. So I tested every multimodal model I could find on Global API. Here's the lineup I ended up evaluating. The Contenders Nine models, three providers, one freelanc

Even if peace holds up between the US and Iran, oil prices aren’t going back down to where they were any time soon.

Getting the future right is now big business. But at a festival in Berkeley, forecasters worry that sports markets could take the whole industry down.

A new report shows that government agencies failed to communicate and includes recommendations for stronger oversight in a bid to avert future disasters.

This week, I covered the story of Casey Harrell—a man with ALS who is “the first power user” of a brain implant, according to the researchers who worked with him. Harrell is paralyzed and unable to speak coherently without the device. He has now spent almost three years using a brain-computer interface (BCI) that enables…

Azure Functions shipped a serverless agents runtime in public preview at Build 2026. Agents are defined in .agent.md markdown files with YAML triggers, MCP server access, 1,400+ connectors, and sandboxed execution. The Functions team confirmed to InfoQ that the runtime adds no cold start overhead and no billing premium beyond standard Flex Consumption. By Steef-Jan Wiggers

A London clinic owner has claimed he is treating people with stage 4 cancer by sealing them into a plastic bag while they're naked from the waist down and gassing them with chlorine dioxide.

Why Taking Feedback Positively Can Change Your Career As developers, engineers, designers, and professionals, we all want to improve. We spend countless hours learning new technologies, building projects, and gaining experience. Yet many people overlook one of the most powerful tools for growth: feedback. Unfortunately, feedback often feels personal. When someone points out mistakes in our code, resume, communication, or project, our first reaction is sometimes defensive. We feel offended, frustrated, or misunderstood. I've experienced this myself. But over time, I learned that the ability to accept feedback positively is one of the most valuable skills anyone can develop. Feedback Is Not an Attack One of the biggest misconceptions is believing that criticism is an attack on our abilities. When a senior engineer reviews your code and suggests improvements, they are not saying you're a bad developer. When a recruiter rejects your resume, they are not saying you're incapable. When users report problems in your open-source project, they are not trying to discourage you. Most of the time, people are simply showing you where improvements can be made. The sooner we separate our ego from our work, the faster we grow. Every Rejection Contains Information Many professionals view rejection as failure. I view it differently now. A rejection is data. If ten companies reject the same resume, the market is telling you something. If users consistently struggle with a feature, they're revealing a usability problem. If interviewers repeatedly point out the same weakness, they're highlighting a skill gap. The goal isn't to feel bad about the feedback. The goal is to learn from the information hidden inside it. Growth Begins Where Comfort Ends Positive feedback feels good. Constructive feedback creates growth. Nobody enjoys hearing that their architecture can be improved, their communication needs work, or their project has flaws. But those uncomfortable conversations often lead to th

Last Tuesday at 3am, a multi-agent loop hit 12K KV writes/minute and froze. The loop was a one-line counter bug. That part was fixable. What I found while tracing it was worse. I had --dangerously-skip-permissions enabled on a Claude Code session that was running D1 migrations. I thought it was pointing at staging. It wasn't — I'd misconfigured my env file reference, loading .env.production instead of .dev.vars . Claude didn't ask. The flag told it not to. The migration was ADD COLUMN , not DROP COLUMN , so no data loss. Survivable. But only barely. The thing I got wrong: I treated --dangerously-skip-permissions as "skip the annoying confirmation popups." It's actually "remove the only moment a human sees what command is about to run." Those are very different things. Turning the flag back off helps, but it doesn't constrain what Claude attempts — it just adds a prompt you'll click through anyway at 3am. What actually worked was adding a deny rule in .claude/settings.json : { "permissions" : { "allow" : [ "Bash(wrangler d1 execute * --local*)" ], "deny" : [ "Bash(wrangler d1 execute *)" ] } } The allow rule is more specific than the deny, so --local calls go through and everything else is blocked before execution. Over 2 weeks post-fix, Claude attempted zero production DB commands. Three deny events were logged — all from ambiguous prompts I wrote during fast context-switches, not from Claude going rogue. I ended up running three layers: the settings.json allowlist, a separate git worktree for migration work that physically contains only staging credentials, and a CLAUDE.md that instructs Claude to ask before anything touching production. The CLAUDE.md approach has a real caveat though — in long sessions the instructions lose weight as context grows. Anything critical needs to be restated in the prompt itself. I wrote up the full breakdown — including the worktree setup, the exact CLAUDE.md wording, and why MCP tool permissions behave inconsistently with the deny ru

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