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Last month, the story broke (alternate link ) that Madison Square Garden uses facial recognition software on everyone entering the facility, and—among other groups—flags activists that oppose using facial recognition. Turns out that the system was shut off for Taylor Swift’s wedding. Evan Greer—one of the people that MSG alerts on— comments : Ironically, Swift herself has reportedly used facial recognition at her own concerts to identify stalkers. This “privacy for me, surveillance for thee” attitude feels like a perfect encapsulation of the future we’re already living in: one where wealthy elites can afford privacy, while the rest of us are forced to live in a corporate surveillance panopticon...

A disputed exam, an unreliable detector, and one very late Apple Pages file.

The company’s first headphones look very stylish and have great tactile features, but underwhelm on sound quality.

Headphone jacks are endangered, but they’re not gone. Here are our favorite smartphones that still let you plug and play.

AI follow-up agent for missed email opportunities Discussion | Link

Sony has received a lot of backlash from PlayStation fans since announcing that it's killing physical game disc production, but that hasn't swayed its decision. During Sony's latest earnings call, chief financial officer Lin Tao said that while the company "put in a lot of thought and time" when considering the opposition put forward by […]

"America won the first Space Race, and we can do it again."

SJY’s Zeph are brilliant wired headphones, using flippable earcups for two different takes on your favorite music.

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July 2026 is over. The month that opened with AUTONOMOUS 2026 and WAIC 2026 running simultaneously on opposite sides of the Pacific closed with the sector tallying what it built. The number that defines the period is $55.8 billion in robotics funding across H1 - nearly double the prior full-year record. But the more durable signal from this week is operational rather than financial: Neura Robotics has a confirmed deployment date at a Schaeffler facility in December, NVIDIA's simulation-to-real pipeline is now functional at production scale, and five simultaneous shifts are reshaping factory floors right now, not in 2027. The questions that drove the first half of 2026 - does Physical AI work, is the funding real, will the robots actually arrive - are no longer interesting. H2 starts with harder ones. Stats: Value Description $55.8B Robotics funding raised in H1 2026, nearly double the prior annual record $8.6B Humanoid startup funding in H1 2026 alone, 1.8x all of 2025 December 2026 Confirmed first deployment of Neura Robotics humanoids at Schaeffler's German facilities 5 Simultaneous operational shifts reshaping factory floors identified in the mid-2026 analysis Neura Robotics Has a Deployment Date: December 2026 in a Schaeffler Factory Most Physical AI deployment announcements are directional. "We are partnering with X to explore robotics in our facilities" is a press release. A confirmed month and a specific facility is a contract. Neura Robotics confirmed that Schaeffler - one of the key investors in its $1.4 billion Series C alongside Amazon, Nvidia, Qualcomm, and the European Investment Bank - plans to deploy Neura's humanoids in its German facilities in December 2026 . Schaeffler manufactures precision bearings and components for electric vehicles, operating in environments where dimensional tolerances are measured in micrometers. Deploying a humanoid robot in that context is a fundamentally different challenge than warehouse pick-and-place or automotive sequ

The slowest job on the content platform I run is authoring a review end to end. Create the record, fill seventy-odd structured fields, write the narrative blocks, upload and wire the screenshots, publish. Every review follows the same shape, which is exactly what makes it miserable and exactly what makes it a good candidate to hand off. The worry people lead with is quality. Will the agent write something embarrassing? That is the easy half. A draft is reviewable, and a bad draft costs nothing but the time it takes to read. Authoring means writing to production, though, and that is a different animal. An agent with write access to a live CMS is not a drafting tool. It is a second admin who never sleeps, never gets bored of the tedious fields, and will work straight through the backlog without ever wondering whether the first record came out right. So the question was never whether an agent could edit the site. It was: what does it authenticate as, what enforces the rules when it writes, and can I reconstruct afterwards what it did. Why a tool server and not a script I weighed three shapes for the write path. One-off REST scripts are the fastest thing to start and the worst thing to own. Each one re-implements whatever slice of the validation rules it happens to need, they do not compose, and nothing tells you later which of them check anything at all. Browser automation is more tempting than it looks, because driving the real admin UI inherits every rule the UI enforces for free. It is also slow, brittle against any markup change, and hands you a screenshot where you wanted a result. I built an MCP server instead. The tools are primitives (get a listing, update a listing, upload an asset, replace a page's blocks) and the agent decides how to sequence them. That was the part worth paying for: I did not have to anticipate the workflows, only the verbs. Roughly forty tools now cover the entity types the CMS manages, and none of them encode a workflow. One server, two t

Two weeks ago I wrote here that I killed my SaaS subscription 7 days after launch and rebuilt it as a buy-once product. I ended that post with a promise written down before I could talk myself out of it: 3 real purchases in 14 days of relaunch, or I move on and leave UIPrompt in maintenance mode. Either way I would post the numbers. The 14 days are up. Here are the numbers. Purchases: 0. New organic signups during the window: 0. The last real signup was a free account three days before the relaunch even went live. They looked once and never came back. So by my own written bar, this is a move-on. UIPrompt goes to maintenance mode today. I want to be useful about why, because "it didn't sell" is a result, not a lesson. What I did in those 14 days Quite a lot. That turns out to be part of the problem. I shipped a real product. The buy-once model was clean: a free playground with no signup, one $39 price, and an AI Design System Pack export that survives a blind test (a fresh AI session got only the exported files and matched 34 of 34 specced properties, inventing zero colors). I bought a custom domain. I launched on Product Hunt with a video, posted a Show HN, cross-posted the pivot article, made a 20-second promo video in Remotion with licensed music, put it on YouTube and X, and submitted to Peerlist, Dev Hunt, Indie Hackers, SaaSHub, and a stack of directories. None of it produced a single sale. Not one. The lesson I did not want On the first launch I blamed pricing. I killed the subscription, and I was right that a burst-usage tool should not bill monthly. But here is the uncomfortable part: fixing the pricing changed nothing, because pricing was never the binding constraint. Demand was. Two different pricing models, same zero, should have told me the problem lived upstream of the checkout page the whole time. I was tuning the part of the funnel I could see and control (the offer) while the actual leak was at the top: not enough of the right people, with pain acute

Approval workflows do not fail only at the model layer. In a production agent, the more common failure is losing the exact paused state that a reviewer was supposed to approve. Why can a saver decide LangGraph approvals? A saver can decide LangGraph approvals because approvals depend on persisted graph state, not just a chat transcript. LangGraph interrupts pause execution inside a node, store the current state, wait until a human decision arrives, and resume the intended checkpoint with Command(resume=...) ; without a saver tied to the same thread_id , the reviewer handoff can resume the wrong point or fail to resume at all . Quick Answer: LangGraph approvals work only when the paused run is checkpointed and resumed through the same thread_id . LangSmith adds the audit layer: each trace is capped at 25,000 runs, and SaaS trace retention is documented as 400 days from ingestion . The practical rule is simple: put the checkpoint before the irreversible action. That means email sends, file writes, deploys, database mutations, support-ticket edits, purchases, payments, outbound messages, and code execution should pause before the side effect. LangChain's HumanInTheLoopMiddleware follows the same shape: inspect tool calls after model output but before execution, then allow an approve, edit, or reject decision against a checkpointed run . "Interrupts are designed to pause graph execution and resume from the saved point," according to the official LangGraph interrupts documentation . For developers, the important part is operational: the approval gate is only trustworthy if the persisted checkpoint and reviewer decision refer to the same run. LangSmith then gives the team evidence that the gate is behaving correctly. Its observability model groups execution into projects, traces, runs, and threads, which lets teams audit latency, rejection reasons, retry count, tool failures, and reviewer decisions instead of debugging from logs alone . The seed video is useful background

Idempotency, vendor failure, monitoring that catches the invisible outages, and the tradeoffs nobody warns you about, lessons from scaling payments infrastructure. Most software fails quietly. A page renders slowly, a recommendation is a little off, a report is stale by an hour. Users shrug and move on. Payments doesn't work like that. When payments break, someone's money is in a place neither of you can account for, and the clock starts ticking on their patience. There's no graceful degradation. Either the money moved, or it didn't, and someone needs to know which. I've spent a good chunk of my career building and scaling payments infrastructure, and it has quietly rewired how I think about engineering in general. Here's what stuck. 📋 The short version # Lesson One-line summary 1 Idempotency You will receive the same request twice. Design for it. 2 Vendor failure Gateways are vendors. Ask "when," not "if." 3 Monitoring Never learn about an outage from a customer. 4 The unglamorous stuff Ledgers, reconciliation, state machines, refunds. 5 Tradeoffs Every lesson above fights at least one other. 1. 🔁 Idempotency isn't a feature. It's a foundation. The first hard lesson: you will receive the same request twice. Not "might." Will. A client times out waiting for your response and retries. A user double-taps a button on a bad connection. A queue consumer crashes after processing but before acknowledging. A gateway sends the same webhook four times because it never got a 200 back. None of these are exotic failure modes, they're Tuesday. If your system treats every incoming call as a new instruction, every one of those scenarios becomes a double charge. And a double charge isn't a bug you fix quietly in the next release. It's a support ticket, a refund, a reconciliation entry, and a customer who now checks their statement every time they use you. The fix is conceptually simple and operationally demanding: every operation that moves money must be uniquely identifiable and sa

If your script suddenly started printing this: >>> import yfinance as yf >>> df = yf . download ( " NG=F " , period = " 1mo " ) 1 Failed download : [ ' NG=F ' ]: YFPricesMissingError ( ' possibly delisted; no price data found ' ) …you didn't break anything. NG=F (the natural gas futures ticker on Yahoo Finance) periodically stops returning data for everyone, and futures tickers get hit harder than stocks. This post covers why it happens and the three fixes that actually work, ordered from "quick patch" to "never deal with this again." 1. What the error actually means yfinance is not an official API . It's a (great) community library that scrapes Yahoo Finance's internal endpoints — the same ones Yahoo's own website uses. Yahoo doesn't document them, doesn't promise they'll keep working, and changes them whenever it suits their frontend. When Yahoo changes something — an endpoint, a rate limit, a response format — yfinance breaks until its maintainers reverse-engineer the change. Futures symbols like NG=F and GC=F are the most fragile: they've had recurring gaps and failures reported over the years, for example #2620 (missing recent data for NG=F/GC=F) , #2635 (whole missing days in futures history) and the evergreen #865 "Futures only work sometimes" . So: "possibly delisted" almost never means delisted. It means "the scrape came back empty." 2. Fix #1 — the quick patches (works today, breaks tomorrow) Three things fix most transient failures: Upgrade first. The maintainers usually patch Yahoo changes within days: pip install -U yfinance Retry with backoff. Failures are often intermittent rate-limiting, not hard breaks: import time import yfinance as yf def download_with_retry ( ticker , retries = 3 , wait = 5 , ** kwargs ): for attempt in range ( 1 , retries + 1 ): df = yf . download ( ticker , progress = False , ** kwargs ) if not df . empty : return df print ( f " attempt { attempt } came back empty, retrying in { wait } s… " ) time . sleep ( wait * attempt ) rai

The breakthrough wasn't a better prompt. It was giving the machine an address. For a long time, I used AI exactly how the landing page told me to. Open tab. Ask brilliant question. Receive eerily competent answer. Steal the good parts. Close tab. Come back 12 hours later to meet a completely new entity with the long-term memory of a goldfish in free-fall. Every session started with the same morning standup for the amnesiac: Here's the project. Here's the stack. Here's what we already tried. Here's the bug you invented yesterday. No, we are not migrating the database at 2am for fun. No, do not rewrite the entire app in Next.js because you felt inspired. Yes, that file you keep ignoring is the entire business logic. It felt productive because words were moving fast. Code poured out. Bullet points bred like mold behind a gas station sink. But the workflow itself was insane. I had a system that could read 10,000 lines of code, hit APIs, run a terminal, crawl docs, and reason across an entire architecture - and I was using it like a genius contractor trapped behind plexiglass at county jail. Every interaction was a visitation. Every visitation required paperwork. The problem wasn't personality. It didn't need a cuter name, a 3,000-word system prompt written like a LinkedIn mantra, or another folder called AI_RULES_FINAL_FINAL.md . It needed continuity. It needed keys to the building. It needed tools, memory, a schedule, a logbook, a junk drawer, and a place where half-finished work could stay half-finished without evaporating. It needed an operating system. Not a literal kernel. Not yet. I don't need my chatbot handling page faults before coffee. I mean operating system in the old, honest sense: a thing that coordinates resources, remembers state, exposes interfaces, enforces limits, and lets processes outlive the conversation that spawned them. The second I started thinking like that, chatting with AI became the most boring thing you could do with it. The Chat Window Is

TL;DR Hello everyone! It's been a while since I've posted a list of interesting projects,...

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