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Uber's CTO told the world this month that the company spent its entire 2026 AI allocation by April. The story has been reported in a handful of outlets, hit the front page of Hacker News for 397 points and 469 comments , and is mostly being read as a cost-of-AI-tools story. It is one. It is also, on a closer reading of the numbers, a pricing-model story — and the structural fact that almost none of the coverage has emphasized is the one that determines whether this is a one-company anomaly or the beginning of an industry-wide budgetary crisis. The structural fact is that Claude Code, like most enterprise AI tooling in 2026, is priced on token consumption, not per-seat licensing. Token-based pricing scales with how aggressively the tool is used. Per-seat enterprise SaaS pricing — the model corporate IT budgets are built around — scales with how many people have access to it. Those two cost curves diverge in exactly the territory where productivity tools are designed to operate: high-engagement, daily-use, gradually-deepening workflows. The Uber data is the first public-facing version of a math problem most enterprise IT departments are about to discover privately. The numbers Uber CTO Praveen Neppalli Naga , named in Yahoo Finance's and Benzinga's coverage, said publicly that Uber is "back to the drawing board" on AI budgeting after the surge in Claude Code use blew through internal projections. The specific numbers, as reported across the multiple outlets covering the story: Claude Code adoption inside Uber's ~5,000-engineer organization went from 32% to 84% over four months. 70% of committed code at Uber is now AI-originated. 11% of live backend updates are "being written by AI agents built primarily with Claude Code," per the reporting. Per-engineer monthly API costs: $500 to $2,000. Uber's annual R&D spend is around $3.4 billion , of which the AI tooling line was a much larger fraction than expected. Cursor adoption plateaued; Claude Code dominated. These are ext

You built a workflow worth sharing — and it works perfectly. Until someone else imports it. The bottleneck is the API key. Use yours, and every user is billed against your account. Use theirs, and they each have to find the credential UI, paste their key, and reconnect every time. Both are friction. The cleaner option is to let the workflow ask for the key on the form, then thread it through to the HTTP nodes that need it. It's simpler than it sounds. This post walks through the pattern with a working credential setup, an alternative for single-node simple cases, the gotchas, and a note on what this enables for custom node authors. The screenshots below come from n8n's built-in Bearer Auth credential and from the n8n-nodes-ldxhub package's own credential schema. The technique itself is generic — what's shown here works for any HTTP-node workflow and any custom node that supports expression-mode credentials. The form asks, the credential listens The simplest case: a Form Trigger collects an API key, then an HTTP node hits an authenticated endpoint with that key. Two nodes, one bridge between them — but the bridge isn't a direct expression. It runs through a credential. The flow: Form Trigger collects api_key (use the Password element type for masking) A Bearer Auth credential references that form input via expression HTTP node picks the credential The Form Trigger is straightforward. Add one field: Form Trigger Form Fields : - Label : API Key - Element Type : Password - Custom Field Name : api_key - Required Field : yes Element type matters. Use Password instead of Text and the input gets masked on screen — the key isn't readable to someone glancing at the browser. Here's the rendered form a user sees when they open the workflow URL: Wiring the credential to expression mode For a Bearer token (which is what most modern APIs use), create a new credential of type Bearer Auth — a generic credential built into n8n that's purpose-built for Authorization: Bearer ... header

Following months of teases, leaks, a secretive debut at NAB 2026, and an initial launch in China, Insta360 finally announced global availability for its first handheld stabilized camera. The Luna Ultra features a pair of 8K cameras atop a 3-axis gimbal, offering an upgrade to DJI's Osmo Pocket 4 and Pocket 4P, which both max […]

We train a model on handwritten digit classification. 99% accuracy . Then we train the same model on a new task — say, fashion item recognition. We go back and test it on digits. 34% accuracy . It has completely forgotten. Not gradually, not partially — almost entirely. What Just Happened? We trained a CNN on MNIST digits — 99.2% accuracy . After fine‑tuning on Fashion MNIST, it reached 91.1% accuracy . But when re‑evaluated on MNIST, accuracy collapsed to 33.9% . This collapse is catastrophic forgetting : the model’s weights shifted to optimize for the new task, erasing the old solution. Why did training on more data make the model worse at something it already knew? MNIST is handwritten digits (0–9). Fashion MNIST is clothing items like shirts and shoes. Both are 28×28 grayscale images, but the tasks are distinct. Why Does It Happen? The core issue is that the model relies on the same set of weights for both tasks. There is no separation or dedicated memory; every parameter is shared . When training shifts from Task A ( MNIST digits ) to Task B ( Fashion MNIST ), gradient descent simply minimizes the loss on the data it sees at that moment. It has no awareness that Task A ever existed. In the loss landscape, imagine two parabolic bowls: one for Task A and one for Task B. The optimum for Task A lies at θ A ∗ ​ , while Task B's optimum is at θ B ∗ ​ . As training on Task B progresses, the weights θ move towards θ B ∗ ​ . This movement inevitably raises the loss for Task A because its minimum is left behind. The root cause is the shared weight space. Gradient descent is a stateless optimizer; it only follows the current gradient signal. Since the minima for Task A and Task B are far apart, there is no single configuration of θ that satisfies both tasks simultaneously. This is why catastrophic forgetting occurs. Weight space can be visualized as an N-dimensional space, where each axis corresponds to one parameter. Every point in this space represents a full set of wei

OAuth for Remote MCP Servers How each AI assistant signs in to a remote MCP (Model Context Protocol) server, and why the flow differs by client and by where it runs. Overview The protocol throughout is standard OAuth 2.1 — an open, widely implemented authorization standard. The human sign-in runs through oauth2-proxy , one of the most widely deployed open-source auth proxies; the only deployment-specific piece is a thin, spec-conforming authorization server (the /oauth endpoints) that hands MCP clients their tokens. Every client ends up the same way — a person signs in against Google (restricted to your organization's domain), and the client holds a short-lived bearer token it presents on each /mcp call. Two things differ between assistants: where the client runs (a machine on the VPN — private — vs. the vendor's cloud — public ), which decides the host it reaches; and what kind of OAuth client it is — a public client proving itself with PKCE (Proof Key for Code Exchange, which lets a client with no secret prove the token request comes from the same client that started the flow), or a confidential client proving itself with a secret. The participants oauth2-proxy — the public-facing reverse proxy. It authenticates the human against Google (the sign-in restricted to your organization's domain) and forwards the verified identity to the app behind it. Only oauth2-proxy faces the internet. It is a mature, heavily-deployed open-source project — the standard way to put Google/OIDC (OpenID Connect) single sign-on in front of a service, widely used in Kubernetes deployments — so the most security-sensitive leg of the flow (the OAuth exchange with the identity provider) runs on battle-tested code. The MCP server — the app on a loopback port behind the proxy. It plays two roles: the OAuth authorization server ( /oauth/authorize , /oauth/token , /oauth/register , .well-known discovery) and the /mcp tool endpoint. It mints codes and tokens, and validates a token on every /mcp c

Electricity demand from AI data centers is pushing everyone — including automakers like GM and Ford — into the energy storage business.

I spent a weekend wiring Google's Gemini and Veo APIs into a single app just to feel where the edges of multimodal AI actually are. It turned into a small studio I now use daily, and along the way I learned more about these models from plumbing them than from any paper. Here's the honest technical debrief. Three pipelines, three completely different problems I wanted one prompt box that could do video, image editing, and document Q&A. Naively I assumed they'd share most of the stack. They don't. 1. Image-to-video: the enemy is time, not pixels Generating one good frame is solved. Video is about temporal coherence — frame 13 must agree with frame 12 or you get flicker and identity drift. Modern video models treat the clip as one object in space and time (latent diffusion over a width x height x time volume, with spatiotemporal attention) rather than 120 independent images. Conditioning on a reference image as the first frame is what makes image-to-video feel controlled: you've handed the model a strong anchor and asked it to extrapolate motion, not invent a world. The surprise: native audio sync (Veo 3.1 generating clip + soundtrack jointly) does more for perceived realism than another notch of resolution. A door slam landing on the exact frame the door shuts is uncanny in a good way. 2. Instruction-based image editing: preservation is the hard part Generating is unconstrained; editing must change one thing and preserve everything else. Condition the diffusion model on both the instruction and the source image's latents, cross-attend the instruction to steer only the referenced region, and bias hard toward preserving unedited latents. Push that preservation too soft and the subject's face quietly morphs across edits — the classic 'character consistency' failure that makes or breaks storytelling use-cases. 3. PDF chat: it's retrieval, not a long context The naive 'paste the whole PDF' approach dies on long files (models get lost in the middle ) and costs you the full

submitted by /u/goto-con [link] [留言]

Fusion power startup Avalanche Energy said its reactor prototype heated a plasma to over 10 million degrees C.

Adi Polak discusses the architecture required to transition from stateless prompts to state-aware, context-rich AI agents. Drawing on 15 years in distributed systems, she shares how engineering leaders can leverage Apache Kafka and Flink for real-time stream processing, dynamic memory tiering, and tool orchestration via MCP to solve token limits, cost spikes, and latency bottlenecks. By Adi Polak

Ambrosia Energy wants to build power plants in less than 12 months while undercutting natural gas. It hopes to build gigawatts worth by 2030.

Meta has to open up WhatsApp to third-party AI chatbots again, the European Commission says.

Doctor Who's previously-announced Christmas special is no more as the BBC severs ties with Russell T. Davies and Bad Wolf.

Im investigating an idea i had about JSX for webcomponents after some experience with Lit. I am sharing this here because it might be interesting/educational for someone, if it isnt, let me know and i'll remove the post. Lit is a nice lightweight UI framework, but i didnt like that it was using class-based components. Vue has a nice approach but i prefer working with the syntax that React uses. I find it more intuitive for debugging and deterministic rendering. I wondered if with webcomponents, i could create a UI framework that didnt need to be transpiled. Read the docs Checkout the code Storybook demo (My intentions with this framework is to get to a reasonable level of stability, to then replace React on some of my existing projects.) IMPORTANT: Im not trying to promote "yet another ui framework", this is an investigation to see what is possible. You should not use this framework in your own code. It is not production-ready. It is not on NPM. Im not looking for another framework to replace React (im trying to create it). This framework is intended for myself on my own projects. This project is far from finished. Feel free to reach out for clarity if you have any questions. submitted by /u/Accurate-Screen8774 [link] [留言]

The RAMageddon crisis has got Microsoft rethinking its Xbox console hardware business. Xbox CEO Asha Sharma and Xbox strategy chief Matthew Ball have both revealed this week that Microsoft is reevaluating plans for its next-generation Project Helix console and exploring "radically different" console business models in the meantime. "We are working very hard to rethink […]

You now can get a US phone number with NordVPN's Saily eSIM app.

Laptops for college should be portable, offer long battery life, and remain reasonably affordable. Based on testing hundreds of laptops, these are my top picks.

Hello Reddit I've been working on QSPR (Quantitative Structure-Property Relationship) analysis for chemical compounds mentioned in the Jean-Claude Bradley Open Melting Point Dataset . Basically the idea is to see how accurate a model can predict melting points of compounds using only topological indices. After some work on the topological indices (feature engineering), each compound was represented by 26 features. I trained a random forest model on the data and got a test r2 score of 0.66 (which is pretty respectable, given the constraints). However, the file size of the model was around 1.23GB. I didn't like it being that big, so I opened up PyTorch to build a custom deep learning architecture that could make predictions as accurately as the random forest but with much smaller file size. After around 2 weeks of research, I build a 270,000 learnable parameter model (1.3-1.4MB according to torchinfo) that got an r2 score 0f 0.6399. Given all this context, I wanted to ask the following question: Should I commit and work on publishing the results, or should I keep working on improving the model? Note: I'm obligated by my university to not give out intricate details of my research before publication, so please forgive me if such details are required for a high quality answer. However, I can give out the metrics achieved by my little deep learning model. Here it is: === Evaluation Metrics (Expected Value) === R² Score : 0.639910 MAE : 41.246754 MSE : 2989.062744 RMSE : 54.672322 NRMSE : 0.083469 MAPE : 11.69% The unit for MAE, MSE, RMSE and NRMSE is Kelvin (K). submitted by /u/AgiGamesYT [link] [留言]

I've written SQL for over a decade. Joins, subqueries, recursive CTEs – the whole deal. Then I tried a graph database (Neo4j). And my relational muscle memory kept getting in the way. I kept trying to "map foreign keys" instead of just… traversing edges. So I built myself a side‑by‑side cheat sheet: SQL → Cypher for everything from basic SELECTs to variable‑length paths that would require recursive CTEs in PG/SQL Server. Turns out, queries like "users who bought this also bought…" go from 30 lines of self‑joins to 6 lines of zig‑zag pattern matching. If you've ever felt frustrated with: multi‑hop join performance LIKE '%...%' on string scans or just the sheer noise of mapping join tables for many‑to‑many …give this 5‑min read a shot. The mental shift alone (relationships as physical edges, not ID matching) changed how I model data – even when I go back to SQL. submitted by /u/ostwal [link] [留言]

Maybe it wasn't a great idea to let 14-year-olds post videos to Spotlight in the first place.