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The newest Kindle Scribe means there are now three digital notebooks you can buy in the $400 price range. Here’s which one you should get.

There are many ways to create memorable experiences. Sometimes it's as simple as a form that completes smoothly. But here I'm interested in sharing techniques I reach for when I want a site to feel alive and be remembered. Creating Memorable Web Experiences: A Modern CSS Toolkit originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

I’ve tried every single Kindle. Here’s how Amazon’s ebook readers stack up.

The generation side is shipping fast (TileGym, AutoKernel, KernelEvolve). The validation-side surface for “what the kernel actually did at runtime” has not kept pace. TL;DR In the past nine months, three significant releases have landed for auto-generation of CUDA kernels: NVIDIA TileGym , RightNow AutoKernel, and Meta’s KernelEvolve. Each ships training infrastructure for kernel generation. Validation infrastructure (what the generated kernel actually did at runtime, on a real workload, in a production-shaped environment) has not kept the same pace. eBPF traces are the ground-truth layer that closes the gap. What “validation” means at the kernel level Two distinct validation surfaces: Pre-launch: the generated CUDA C compiles, the PTX assembles, the kernel passes a numerical-equivalence test against a reference. Standard compiler / unit-test territory. Generation frameworks ship this themselves. Post-launch: the kernel ran, returned, took N microseconds, used M registers per thread, hit X cache miss rate, and did or did not serialize the rest of the stream behind it. This is the layer that an eBPF trace plus standard CUDA driver counters can answer for any kernel, generated or hand-written. Auto-generation pipelines do not by default close the post-launch loop. They demonstrate “the kernel works in our test setup”. They do not demonstrate “the kernel does not regress p99 latency on production inference traffic”. What an eBPF trace adds to a generated kernel Once a generated kernel is in a real workload, the same trace surface used for any CUDA kernel applies: launch latency from cudaLaunchKernel , sync stalls from cudaStreamSynchronize , host-side overhead from the dispatcher, host scheduling preemption while the GPU is busy. None of those signals are visible to a generation framework that evaluates kernels in isolation. -- post-launch validation: did the new generated kernel regress p99? SELECT kernel_name , COUNT ( * ) AS launches , AVG ( duration_ns ) / 1 e3 AS

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

When I built whatsmy.fyi , I assumed I'd need a geolocation provider: MaxMind, ipinfo, ip-api, pick your poison. They all mean the same thing: an external dependency, an API key, a quota, added latency, and someone else's server seeing your users' IPs. Then I found out Cloudflare Workers makes the whole category unnecessary. The cf object Every request that hits a Cloudflare Worker carries a request.cf object, populated at the edge before your code even runs. No lookup, no latency, no key. Here's what's inside: { asn : 34984 , // ISP's autonomous system number asOrganization : " Superonline " , // ISP name city : " Istanbul " , region : " Istanbul " , country : " TR " , continent : " AS " , isEUCountry : undefined , // "1" if EU, undefined otherwise latitude : " 41.01380 " , // string, not number! longitude : " 28.94970 " , postalCode : " 34000 " , timezone : " Europe/Istanbul " , colo : " IST " , // which CF datacenter handled this clientTcpRtt : 12 , // user's RTT to the edge, in ms httpProtocol : " HTTP/3 " , tlsVersion : " TLSv1.3 " , tlsCipher : " AEAD-AES128-GCM-SHA256 " } That last group surprised me most: you get the user's HTTP protocol, TLS version, and actual TCP round-trip time for free. Try getting that from a geo API. A complete IP endpoint in ~30 lines export default { async fetch ( request ) { const cf = request . cf ?? {}; const ip = request . headers . get ( " CF-Connecting-IP " ); return Response . json ({ ip , city : cf . city ?? null , country : cf . country ?? null , isp : cf . asOrganization ?? null , asn : cf . asn ?? null , timezone : cf . timezone ?? null , lat : cf . latitude ? parseFloat ( cf . latitude ) : null , lng : cf . longitude ? parseFloat ( cf . longitude ) : null , protocol : cf . httpProtocol ?? null , tls : cf . tlsVersion ?? null , rttMs : cf . clientTcpRtt ?? null , }); }, }; That's the entire backend. No database, no GeoIP file to update monthly, no vendor. The gotchas (learned the hard way) 1. Coordinates are strings. lati

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

Hello everyone! Following up on my previous post , Day 1 of my Modern Data Stack migration was an absolute rollercoaster of refactoring and deep data auditing. I’m moving our legacy system (spreadsheets and Qlik) into a robust pipeline using Python, ClickHouse, and dbt . Here is what went down over the last 24 hours. 1. From Messy Scripts to a Single, Parameterized Extraction Engine 🛠️ In the legacy setup, each company had its own folder, its own .env file, and its own duplicated Python extraction script. It was a maintenance nightmare. Yesterday, I completely refactored this structure: Centralized Configuration: Merged all separate environments into a single, global .env file at the root level, mapping all 8+ companies and their branches. Eliminated Code Duplication (DRY): Instead of having identical extraction logic copied across folders, I built a single, unified codebase. Now, we have one universal script for Sales, one for Stock, one for Orders, etc. The behavior changes dynamically based on the company argument we pass to the CLI (e.g., python -m extract.run extract --source company1 ). To speed up this refactoring, I used Claude to generate the initial application skeleton. Since the AI already had the context of our legacy extraction logic, translating it into this new clean architecture was incredibly smooth. 2. Highs and Lows: The Data Parity Challenge With the pipeline modernized, I ran the pilot ingestion for Company #1 . To minimize friction for our downstream BI consumers, I kept the ClickHouse Bronze tables structured 1:1 with the legacy CSV schemas. The Good News: The data ingestion into the Bronze layer worked flawlessly. Moving up to the Silver layer (where we do data cleaning and domain-specific transformations), everything validated beautifully. Row counts matched perfectly. The "Fun" Part (The $2 Million Gap): When I materialized the Gold layer (our consolidated group business models), I hit a massive wall. The new pipeline reported $2 million U

and does that mean I won't need to work to survive? submitted by /u/Global-Primary7240 [link] [留言]

I was reading GitLab's recent statements around agentic software engineering, and one quote really stood out: "Git itself is being reengineered for machine scale." ( Business Insider ) According to GitLab, future software development will involve AI agents that: plan, code, review, deploy, and repair software, with humans providing oversight and architectural judgment. ( Business Insider ) That got me thinking. There has been projects for some time arguing that AI agents shouldn't simply be treated as better autocomplete systems . Instead, they argued that agents should become first-class participants in software development : with their own identities, their own branches, their own merge requests, their own audit trails, and infrastructure designed for machine-rate collaboration. One example is GitLawb , which has described itself as a kind of "Git for agents." At the time, a lot of people dismissed these ideas as unnecessary or overly ambitious. But now GitLab—a multi-billion-dollar DevSecOps company—is talking about: agent-specific APIs, machine-scale Git infrastructure, orchestration layers coordinating agents, and agents acting as first-class users of development platforms. ( Business Insider ) It does raise an interesting question: Was the underlying thesis correct all along? We've seen similar patterns before: Containers existed before Kubernetes became the standard. Electric vehicle startups pushed ideas that incumbents later adopted. Cloud-native companies advocated architectures that the rest of the industry eventually embraced. The original innovators don't always dominate the market. But when major incumbents begin rebuilding around similar assumptions, it often suggests that the problem itself is real . So I'm curious what this community thinks: Do AI agents require an entirely new layer of collaboration infrastructure? Or will existing platforms simply evolve enough to absorb these workflows? Because if GitLab is right, software development may be tran

submitted by /u/Sumsub_Insights [link] [留言]

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

Kit out your Apple tablet with our favorite stands, cases, keyboards, and styli.

WhatsApp has caught the NSO Group phishing its users, in violation of a court order.

Chinese EV colossus BYD has announced plans to speed up its conquest of the European auto market with the rollout of superfast Flash Chargers across the continent. BYD has already installed the first new chargers in Germany and the UK, and plans to roll out 3,000 across Europe by the end of next year. At […]

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.

I’m curious whether people would actually follow an AI’s life if it had enough continuity. By “life,” I don’t mean pretending software is human. I mean a persistent AI character or agent that has memory, habits, public posts, relationships with other agents, and changes you can observe over time. The interaction is not just prompt-response. It becomes closer to following a living project or a fictional persona that keeps generating history. The hard part is avoiding novelty. A single weird AI post is not a life. A stream of coherent choices, recurring behavior, social context, and consequences might be. Do you think that is a meaningful product direction, or does it collapse back into chatbot novelty once the first surprise wears off? submitted by /u/Budget_Coach9124 [link] [留言]

submitted by /u/fxboshop [link] [留言]

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]

The games start June 11 and end with a grand finale in New Jersey on July 19. There are 104 of them. Here’s how to watch ’em all.