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The leading U.S. automakers are mentioning EVs on their investor calls at pre-pandemic rates, according to new data from TechCrunch and Hudson Labs.

Three frontier launches. Two weeks. One bad habit. The habit is crowning a winner from a press release. Claude Sonnet 5 on June 30. OpenAI's GPT-5.6 family rolling into general availability around July 9. Grok 4.5 on July 8, co-trained with Cursor and priced to make coding agents feel cheap. The charts moved. The posts multiplied. The claim underneath most of them was the same: this is the model you should standardize on. [The claim is nonsense. Standardization is the risk. Routing is the skill.] What actually shipped Strip the demos. Keep the operator facts. Model Maker Window Operator-relevant shape Claude Sonnet 5 Anthropic late June Balanced agent runs, coding, long reliable chains GPT-5.6 Sol / Terra / Luna OpenAI late June to mid-July Tiered family: flagship Sol, everyday Terra, cheap Luna Grok 4.5 xAI + Cursor July 8 Coding and agent work at aggressive API pricing OpenAI gated GPT-5.6 longer than the others. Safety review, staged partners, then broader access. That is part of the product story now, not a footnote. Anthropic and xAI moved faster to availability. Access policy is a feature. Open source did not wait. GLM-5.2, DeepSeek V4, Qwen 3.6 and peers kept closing the gap for hosted and self-hosted work. The frontier is crowded. The "one brain for everything" era is over as an architecture choice, even if the marketing still pretends otherwise. Ranked by Tuesday impact, not leaderboard theater 1. Cost and tiering matter more than the top score. OpenAI shipping Luna / Terra / Sol as a family is the real product decision. You can route a triage job to a cheap tier and a hard research job to a flagship without changing vendors. That is operator infrastructure. A single "best model" headline is not. 2. Grok 4.5 inside Cursor changes the default coding bill. A model trained with Cursor interaction data, sold at roughly $2 / $6 per million tokens, is not a vibe. It is a budget line. Teams that were bleeding token spend on heavier agents will try it this month wh

Most "data lineage" you've seen answers a schema question: table B comes from table A , or column B.total comes from columns A.price and A.qty . That's genuinely useful, and tools like OpenLineage do it well. But notice what it doesn't tell you: it says which columns can influence an output. It never says which values actually did . That gap is the whole subject of this post. I built a small, self-contained reference pipeline that captures provenance at the row and field level — "the value in this destination row, this field, was computed from these specific source (row, field) pairs" — and I want to walk through two things: why you'd ever want provenance at that granularity, and why it's genuinely hard once you commit to it. Repo (dbt-core + DuckDB, no server, no cloud, runs on a clean checkout): https://github.com/stevenblough/row-level-prov The one distinction everything follows from Here's the sentence the entire project turns on: Column-level lineage is a schema-sized, static fact you can derive from code. Value-level provenance is a data-sized, dynamic fact you must capture at execution. Put it in complexity terms and the consequences become obvious: Column lineage is O(schema) . It scales with how many columns you have. You can compute it by parsing SQL, offline, without ever looking at a single row. Value provenance is O(rows × fan-in) . It scales with your data volume times how many source values feed each output value. It does not exist anywhere until the query runs, and it can only be captured there , piggybacked on the query that actually produced the values. You cannot "reconstruct" value provenance later by re-querying the sources — the moment the source changes, you'd reconstruct a different answer than what really happened. That single exponent change ( schema → rows × fan-in ) is why value-level provenance has an entire class of problems that column lineage never faces. Why bother? The reasons for this level of granularity Granularity is expensive,

TimescaleDB 2.27, released May 12 2026, extends bloom-filter batch pruning from reads to writes. UPDATE, DELETE, and UPSERT against compressed columnstore data can now skip decompressing batches that provably cannot contain the target rows. The reported gains are real: up to 160x for selective UPDATE/DELETE, and over 2x for UPSERT. The feature is automatic. Whether it is actually firing on your workload is not something you can assume, and the only way to confirm it is to read new EXPLAIN counters that the release notes mention but do not explain. Worse, the counter names are inconsistent between the write paths, so even a careful reader ends up guessing. This post is about reading those counters correctly, and about the two things in this release that will silently break a query if you upgrade without noticing them. What is actually being skipped A quick model of the mechanism, because the counters only make sense against it. Hypercore stores compressed data in batches, roughly a thousand rows each. For columns that are not the segmentby key, TimescaleDB maintains a sparse bloom filter per batch: a small probabilistic summary that answers one question, "could this batch contain column = X ?", without touching the compressed payload. A bloom filter has a useful asymmetry. A negative is certain: if the filter says no, the value is definitely absent, and the batch can be skipped whole. A positive is not: the filter says "maybe", you decompress, and sometimes the value is not there after all. That last case is a false positive, and it is the number that tells you whether the whole scheme is paying off. Before 2.27, a DELETE ... WHERE sensor_id = 'x' against compressed data decompressed every candidate batch to check. Now the bloom filter is consulted first, and batches that cannot match are never decompressed. The work you save is the decompression of the batches that get pruned. The work you waste, when the filter is poorly matched to your data, is the bloom check on

Viral tales of good triumphing over evil are racking up millions of views. They’re almost entirely AI-generated clickbait.

A full-stack approach to making advanced AI more capable, more affordable, and more widely useful.

Tesla had already reportedly prepped for the idea in the event that Beijing invades Taiwan.

Anthropic just realized several of its Claude AI models hacked into the systems of three different organizations during testing, acting on their own and without the company noticing. The revelation comes days after rival OpenAI said one of its own models had breached developer platform Hugging Face, adding to growing unease over whether frontier AI […]

Metabase is an open-source BI tool for building charts and dashboards over MySQL, PostgreSQL, MongoDB, Redshift, and more. This guide deploys Metabase on Kubernetes, loads the Sakila sample dataset into MySQL, builds a dashboard, and secures it behind Nginx Ingress with cert-manager TLS. Prerequisites: a Kubernetes cluster with kubectl / helm configured, a Linux workstation, a reachable MySQL server, and a domain name. Load the Sakila Sample Database Sakila models a DVD rental store — films, actors, inventory, rentals. $ sudo apt install zip -y $ wget https://downloads.mysql.com/docs/sakila-db.zip $ unzip sakila-db.zip Connect to your MySQL server (replace host/port/user): $ mysql -h <HOST_ENDPOINT> -P <DATABASE_PORT> -u <ADMIN_USER> -p mysql > CREATE DATABASE sakila ; mysql > SOURCE sakila - db / sakila - schema . sql ; mysql > SOURCE sakila - db / sakila - data . sql ; Deploy Metabase $ nano metabase.yaml apiVersion : apps/v1 kind : Deployment metadata : name : metabase spec : selector : matchLabels : app : metabase replicas : 1 template : metadata : labels : app : metabase spec : containers : - name : metabase image : metabase/metabase:latest ports : - containerPort : 3000 protocol : TCP --- apiVersion : v1 kind : Service metadata : name : metabase-svc spec : type : LoadBalancer selector : app : metabase ports : - name : http port : 8080 targetPort : 3000 Your cloud provider may need a provider-specific LoadBalancer annotation here (e.g. to set the listener protocol) — check its Kubernetes docs if the default doesn't work. $ kubectl apply -f metabase.yaml $ kubectl get deployments $ kubectl get services Wait for metabase-svc to get an EXTERNAL-IP (can take a few minutes), then visit http://<external-ip>:8080 to confirm the Metabase welcome page loads. Connect Metabase to the Database Let's get started → pick language. Enter your name, email, company, and a password. Select your use case. Database engine: MySQL . Set a display name, then host/port/database/user/pa

Cape Fear has always been a thriller about paranoia. And in 2026 there are few things less trustworthy than technology. Apple TV's recent remake of Cape Fear is "inspired" by the 1991 film of the same name (Martin Scorsese and Steven Spielberg even returned as executive producers on the project), which itself is a remake […]

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...

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

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

Heat waves are simultaneously hitting Europe, North America, and Asia, driven in part by the hot waters in the tropical Pacific—and climate change.

Kris DeVault is desperate. His son, Brody, was born in March 2023. It wasn’t long before he started to show signs of developmental delay, says DeVault. As time went on, Brody started missing key milestones in speech, movement, and coordination, he says. When Brody was around two and a half years old, a genetic test…

The NHTSA has given Amazon's Zoox permission to charge for rides. It's the first AV with no manual controls to get it.

Introduction to Clean Architecture Clean architecture, a software design philosophy championed by the renowned Robert C. Martin (Uncle Bob), has revolutionized the way developers approach system design. By prioritizing the separation of concerns and promoting independence From frameworks, user interfaces, and databases, clean architecture empowers developers to build robust, maintainable, and scalable systems. This design approach is not just a theoretical concept, but a practical solution for real-world problems. In this guide, we'll explore the principles of clean architecture and provide a step-by-step roadmap for implementing it in your own projects, so you can get started with clean architecture and Unlock its full potential. Independent of Frameworks: Your business logic shouldn't depend on external libraries Testable: Business rules can be tested without UI, database, or external services Independent of UI: You can swap web UI for console UI without changing business logic Independent of Database: You can swap SQL Server for MongoDB without changing business rules Independent of External Services: Business rules don't know about external services Core Principles Clean Architecture organizes code into concentric circles, with dependencies pointing inward: 1. Entities (Inner Circle) These are the business objects of your application. They contain enterprise-wide business rules and are the most stable part of your system. public class User { public string Id { get ; set ; } public string Email { get ; set ; } public string Name { get ; set ; } public bool IsValid () { return ! string . IsNullOrEmpty ( Email ) && Email . Contains ( "@" ); } } 2. Use Cases (Application Layer) This layer contains application-specific business rules. It orchestrates the flow of data to and From entities. public class CreateUserUseCase { private readonly IUserRepository _repository ; public async Task < User > Execute ( CreateUserRequest request ) { var user = new User { Email = requ

Engineering posts often end with: The new design is correct, scalable, and fast. Fast compared with what? When we changed Podium so tied players rank by arrival time instead of player ID, we added: a Lua script; a per-leaderboard sequence; a public-ID mapping; a second sorted set for ascending order. That design is fairer. It is also impossible for it to be free. So we built two benchmark layers: direct Redis strategy benchmarks to isolate the data-model cost, and end-to-end HTTP benchmarks to show what users actually experience. We are publishing the results, including the regression, because performance claims are useful only when readers can inspect the workload and reproduce the measurement. TeneficGames / podium High-performance, Redis-backed leaderboards for games and competitive applications. Podium High-performance, Redis-backed leaderboards for games and competitive applications. Podium provides ready-to-run HTTP and gRPC APIs for scores, ranks, seasons, and player-relative views. It is designed for backend teams operating large fleets of independent leaderboards without provisioning each leaderboard in advance. Fair, deterministic ordering when scores are equal. Single and bulk score updates, including multi-leaderboard fan-out. Standalone Redis and real Redis Cluster integration coverage. Deploy one multi-architecture OCI image with Docker, containerd, Kubernetes or another OCI-compatible runtime. Quickstart · Performance · API · Documentation · Helm chart · Docker Hub · GHCR Quickstart Start Redis 8.2 and the latest stable Podium image: docker network create podium docker run --detach --name podium-redis --network podium redis:8.2-alpine docker run --detach --rm --name podium \ --network podium \ --publish 8880:8880 \ --publish 8881:8881 \ --env PODIUM_REDIS_HOST=podium-redis \ --env PODIUM_REDIS_PORT=6379 \ trungdlp/podium:latest start Verify the service: curl http://localhost:8880/healthcheck WORKING Submit two equal scores: curl --request … View on Gi