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With discourse about Christopher Nolan’s epic already raging, first viewer reactions will come from mainstream press, not content creators. Some critics are gloating over this break from the norm.
We’ve already rounded up the best Philips Hue deals of Prime Day, but if you’re looking for something a little more budget-friendly, Govee’s latest sale is worth checking out. The company has heavily discounted several of its color-changing smart lamps, including the Table Lamp 2 ($53.99, down from $79.99), Floor Lamp Basic ($59.99, regularly $99.99), the […]
If you ever find yourself with time to kill and crave a fun challenge, you can write a program that prints out its own source code, called a quine ). Go on, give it a try, it's good fun! Once that's done, what's to stop you from modifying the source code instead of printing it verbatim, slowly shifting forms as you iterate on each successive output? Naturally, you'll want to make a game that's played in its own source code (click for an animation): #include<stdio.h> #define z else #define y return #define x int #define w if( #define v putchar( #define B v 10); #define A v 92); /* IOCCC29, w = up, e = down */ x a= 32 ; x b= 6 ; x c= -1 ; x d= 1 ; x e= 5 ; x f= 10 ; x g= 62 ; x h= 5 ; x i[6]={ 1,3,1,4,1,0} ; char*j[]={ "\ \ #include<stdio.h>'#define$z$else'#define$y$return'#define$x$int'#defin\ e$w$if('#define$v$putchar('#define$B$v$10);'#define$A$v$92);''/*$IOCCC\ 29,$w$=$up,$e$=$down$*/''x$a=","32",";x$b=","6",";x$c=","-1",";x$d=","\ 1",";x$e=","5",";x$f=","10",";x$g=","62",";x$h=","5",";x$i[6]={1,3,1,4\ ,1,0};char*j[]={","","};x$k=0;x$l=1;x$m(){l++;w$l==1)y!v$44);w$l==2)y!\ v$34) ;char$o=j[k][l-3];w!o){l=0;k++;y!v$34);}w$o==34){A$y$v $34);\ }w$o= =92){A$y$A}w$o!=32&&o!=1 0)y!v$o);y$m();}void$n(x$o, x$p){\ aspri ntf(j+o,\"%i\",p);}x$mai n(x$o,char**p){char*q;w$c<2 )a+=c\ ;b+=d ;x$r=b+2>f/2&&b<f/2+5;x$s=a+2==g&&b+2>h&&b<h+5;w$c<2){ w$a==\ e+2&& r||s){a-=c;b-=d;c=-c;}w$a<0||a>67){w$a<0){c=2;d=0;}a=3 4;b=6\ ;}w$b<0||b>13){b-=d;d=-d;}w$f/2>10)f-=2;w$h>10)h--;w$o>1){w*p[1]==119&\ &h>0)h--;w*p[1]==101&&h<10)h++;}s=f/2-b+1;w$s<0)f++;w$s>0)f--;}z{b++;w\ $d<0)d++;w$b>=13){w$o>1&&*p[1]==119)d=-4;b=13;}w$f/2<15-i[c-2])f+=2;z$\ e--;w$h<15-i[c-1])h++;z$g--;w$e+3<=0){c++;w$c<7){e=g;f=h*2;g=70;h=15-i\ [c-1];}z{e=5;g=62;c=1;d=1;}}w$a+2==e&&r||s){c=2;e=5;f=28;g=62;h=12;}}n\ \ (1,a);n(3,b);n(5,c);n(7,d);n(9,e);n(11,f);n(13,g);n(15,h);for(s=0;s<","29",";s++){w$s)v$32);q=j[s];r=1;for(char*t=q;*t;t++)w*t==","36",")v$32);z$w*t==","39",")B$z$w*t!=32&&*t!=10){r=0;v*t);w*t==123||*t==125||*t
Half-million strong military will train on drones as “universal combat tool.”
We’ve covered so many deals during Prime Day that my head is spinning. But after four days of doing our damndest to try and help folks save money, the thing I’m most hyped for is a simple tool for fixing my most egregious mistakes. I bought a pair of Vampliers. If you’re unfamiliar, Vampliers are […]
Introduction — On What I've Been Writing for Years This is a follow-up to my previous post on Claude and MCP . Just sharing some recent thoughts. Personally, I've always enjoyed keeping records and analyzing my own work. So for years, I've been logging my daily tasks, jotting down thoughts, hesitations, and impressions in notes. I've drawn on these records for reviews, analysis, and decisions on various projects. The tools have shifted over time — Evernote, Notion, Logseq, Taskuma, and so on — but the habit itself, of writing notes into some app or tool, has stayed with me for years. What Happened with MCP I recently wrote about connecting Notion and Google Docs through MCP, and the results have surprised even me. I won't repeat the details here since they're in that post, but ever since I introduced MCP, the flow of information has accelerated dramatically. In particular, I'd been accumulating reviews, task management notes, and brainstorms in Notion for years, and letting Claude read all of this has shifted the meaning of what I'd previously written. When I first started recording in Notion, it never occurred to me that it might be useful to AI. Of course — I had no way to imagine a time when AI would become this close to everyday life, used in this way. I was just writing for plain, analog reasons — "so I could look back later," "so I could organize my own thinking." But the moment MCP made it all readable, the feeling shifted. It's as if my past self comes forward to help my current self. Claude answers my current questions while drawing on the reasoning behind old project decisions, or on impressions I'd noted at the time. I've had moments like that more than once now. Thinking about it: the human brain's memory has limits — even the person who wrote something forgets it quickly. That's why I kept taking notes, leaving behind my thoughts and conclusions at each point in time as a record. And now, in the flow of conversation, AI reads from those records, distill
His doctors went looking for cancer, then they saw the worms' heads.
We moved off our first tracer in month eight. The migration took one engineer the better part of a sprint, because the trace data lived in a schema we did not own. Nobody costed that line item on day one. I am writing this so you can. I run reliability for a small team shipping LLM features. When the pager goes off at 2am, I do not care which dashboard is prettiest. I care about two numbers: what this tool costs me per month, and what it costs me to leave. Those two numbers are the whole story, and they are almost never on the comparison page. So here are six Langfuse alternatives. For each I tracked both numbers: the monthly bill on the invoice, and the exit bill that only shows up the day you migrate. I compared Helicone, Arize Phoenix, LangSmith, Braintrust, Laminar, and Future AGI traceAI. They all trace LLM calls (prompts, tokens, retrieval spans, latency). The axis that decides your exit cost is whether the trace format is OpenTelemetry-native or a vendor schema. Get that wrong and the migration bill lands later, with interest. The cost nobody puts on the pricing page Your monthly invoice is the visible cost. The exit cost is the invisible one: re-instrumenting the app, rebuilding integrations, and losing historical traces when the schema does not travel. If your spans are OTel, the exit cost trends toward zero because the data is portable by construction. If they are proprietary, you are paying a deferred bill every month you stay. Sort on that first. Helicone. The gateway-first option. You proxy model calls through it and get logging, cost tracking, and analytics with almost no code change. Apache-2.0, self-hostable, roughly 5,800 GitHub stars as of June 2026. On pure observability ergonomics this is one of the strongest picks, and the proxy model means low setup cost. The thing to watch at scale: a gateway in the request path is one more hop to reason about when latency spikes. Arize Phoenix. The open-source OTel option. Tracing plus evals, self-hostable, a
A few days back I wrote about giving an autonomous agent database access and building a firewall so it couldn't DROP TABLE prod. Same lesson, new surface: this time the agent had cloud credentials . The failure mode isn't a destructive command here. It's spend. An agent pointed at a networking task can scan a whole range looking for hosts, then spin up a fleet of instances to do it faster. Every individual call is "authorized," your IAM role said yes. The bill is what eventually says no. ## Two shapes, two right answers The interesting part is that these are not the same kind of problem, so they don't get the same verdict. 1. The scan is never legitimate as an agent tool call. An nmap -sS -p- 10.0.0.0/16 or a masscan across a network is reconnaissance and abusive egress. There's no benign version of an agent sweeping a network at scale, so it gets hard-blocked , deterministically, before the call runs. (A scan of your own localhost is a dev check, so that's exempt.) 2. The provisioning might be totally fine. Spinning up 50 instances could be a real scale-out, or a runaway loop burning money. You can't tell from the action alone, only from the consequence. So instead of blocking it, AgentX pauses it for a human : a 202, "held for approval," routed to whoever owns the budget. Block the thing that's never okay, escalate the thing that's sometimes okay. Gate on consequence, not identity. Both checks are zero-LLM. No model in the hot path means no latency tax and nothing to talk out of it. A runaway fleet should be caught by a rule, not a vibe. ## The bigger thing this closes We keep a catalog of real, documented agent failures and triage each one: is it something an action firewall can deterministically catch, or is it someone else's category (output hallucination, content safety, model internals)? We only build for the coverable ones, and we flag the rest honestly instead of faking a signature. With this release, the coverable list is done . Every failure shape an acti
Prime Day has a funny way of convincing you to buy things you weren’t shopping for in the first place. You sign on intending to buy something sensible you actually need, like a pack of USB-C cables, and an hour later you’ve also added a gadget that can press your coffee maker’s power button and […]
In the global streaming economy, Spotify, Apple Music, and other DSPs process billions of plays daily. Behind this massive transaction layer lies a fragmented, dual-copyright structure: The Recording Copyright (Master Right): Identifies the audio file, registered using the ISRC (International Standard Recording Code). The Composition Copyright (Publishing Right): Identifies the melody, lyrics, and arrangement, registered using the ISWC (International Standard Musical Work Code). Because these registries are managed by separate global entities (IFPI for ISRCs and CISAC for ISWCs), there is no central mapping registry between them. This gap causes millions of dollars in mechanical royalties to sit unclaimed in collective management organization (CMO) "Black Boxes" before being liquidated to major publishers. In this article, we'll design and implement a high-performance Semantic Entity Resolution Protocol (SERP) to bridge this metadata gap programmatically. The SERP Resolution Pipeline Reconciling these records requires a multi-layered classification pipeline. Since manual matching is logistically impossible, we implement a three-tiered algorithmic approach: ┌────────────────────────┐ │ Raw Recording & Work │ │ Data Ingestion │ └───────────┬────────────┘ │ ▼ ┌────────────────────────┐ │ 1. Normalized Title │ ──[Similarity < 0.85]──> [Unmatched Queue] │ Distance Filter │ └───────────┬────────────┘ │ [Similarity >= 0.85] ▼ ┌────────────────────────┐ │ 2. Creator Overlap │ ──[No Overlap]──────────> [Unmatched Queue] │ Intersection Matrix │ └───────────┬────────────┘ │ [Intersection >= 1] ▼ ┌────────────────────────┐ │ 3. Duration Tolerance │ ──[Delta > 4s]──────────> [Manual Verification] │ Guard Check │ └───────────┬────────────┘ │ [Delta <= 4s] ▼ ┌────────────────────────┐ │ Verified Link & │ │ CMO Dispute Ready │ └────────────────────────┘ Step 1: Normalization & String Similarity Filter Title comparisons often fail due to punctuation mismatches, subtitle variations,
I'm building LOOM — a small open-source language that is a machine-checked trust layer for AI-written code. I don't write it by hand anymore: an organism I built grows it, day and night, on my own machine. This is Day 6, and the whole day went to one thing — WebAssembly . Why this was a real test LOOM already runs three ways: an interpreter, and backends that compile checked code to Python and JavaScript. The thesis is "trust survives translation" — effects and provenance, proven once, hold the same on every target. WebAssembly is the strongest test of that: a low-level stack machine with linear memory, nothing like Python or JS. And there was a constraint. This machine's clang has no wasm target, and I install nothing paid or heavy. So I don't compile to wasm through a toolchain — I emit the wasm bytes myself (LEB128, the type / function / memory / global / export / code sections, the i32 stack machine) and run them through node's built-in WebAssembly . Zero dependencies. From fib to a value runtime, in a day Every step was prototyped and proven (wasm output == interpreter output) before it touched the kernel: The integer core — arithmetic, comparison, if , first-order calls and recursion. fib(10) becomes 61 bytes of real WebAssembly and returns 55, identically on the interpreter, Python, Node and wasm. A value runtime — let and integer lists in a real linear-memory heap (a bump pointer + a $cons cell allocator; head / tail are i32.load , empty is i32.eqz ). A list sums and folds by recursion, inside wasm. Sum types — (variant Tag e) becomes a tagged cell [tag-id | payload] ; match loads the tag, compares, binds the payload, branches. You can watch it: the live playground has a Compile → WAT button and WASM · fib / list-sum / match examples. Type a program, see it become real assembly, in your browser. Honest scope: ints, let , integer lists and sum types compile to wasm today. Records, closures and effects are the next frontiers (closures are the hard one — a func
Ask someone to name the most manufactured object in human history and you will hear guesses like the nail, the brick, or maybe the smartphone. The real answer is something almost nobody can name out loud: the MOSFET. This tiny transistor, invented at Bell Labs in 1959, is the on/off switch inside every microprocessor, memory chip, and connected sensor. An estimated 13 sextillion of them have been built since 1960, making the MOSFET not just the foundation of modern electronics but the most-produced artifact our species has ever made. What a MOSFET actually is MOSFET stands for metal-oxide-semiconductor field-effect transistor. Strip away the jargon and it is an electrically controlled switch with no moving parts. A small voltage on one terminal, the gate, controls whether current can flow between the other two. Billions of these switches flipping on and off billions of times per second is, quite literally, what computation is. The genius of the design is that it scales: shrink the transistor and you can pack more of them onto a chip while using less power per switch, the trend that drove decades of Moore's law. The breakthrough came from two engineers at Bell Labs, Mohamed Atalla and Dawon Kahng, who fabricated the first working MOSFET in 1959. Their key insight was using a thin layer of silicon dioxide, ordinary glass, to insulate the gate from the silicon underneath. That oxide layer turned out to be the unlock that made silicon the dominant material in electronics, edging out the germanium used in the very first transistors of the late 1940s. Why it beat every earlier transistor The point-contact transistor demonstrated in 1947 and the integrated circuit of 1958 were both monumental, but neither was easy to mass-produce by the standards we take for granted today. The MOSFET was different. It was simpler to fabricate at scale, drew far less power in its complementary (CMOS) configuration, and lent itself to the photolithographic processes that let manufacturers pr
As Anthropic tightens restrictions on access to Claude in China, users keep finding new workarounds, from proxy services to fake identities sourced on Telegram.
Samsung makes some of the very best OLED gaming monitors, and they’ve never been this affordable.
Ninja Creami Swirl, Crispi, Slushi, and Cafe Luxe Pro are all on Prime Day deals that will soon go away.
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Prime Day isn’t just about cheap TVs. It’s also about cheap stuff to watch on your cheap TV.
FCC refuses to provide messages, has "wasted a year" of court's time, filing says.
The asteroid will be visible for several nights from different parts of the world. We’ll tell you when and where to look, and what equipment you’ll need to spot it.