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Jul 29, 2026 Shared reading
This is a compact argument for returning build systems to first principles: name the inputs, name the outputs, then make the dependency graph and artifact boundaries explicit enough that the build can remain local,...
My notes → Jul 29, 2026 Shared reading
This is the ABI explainer to send to someone who knows that binary wheels are painful but has never seen the contract underneath them.
My notes → Jul 29, 2026 Shared reading
Mitchell Hashimoto gives SIMD the right entry point: recognize the common shape before reaching for cleverness. Broadcast a constant, process a vector-width chunk, reduce the result, and retain the scalar loop for the...
My notes → Jul 29, 2026 Shared reading
Jesús Espino makes Go profiling feel less like a magical command-line ritual and more like five concrete collection systems. The useful distinction is not just CPU versus heap: CPU samples arrive through a signal-driven...
My notes → Jul 29, 2026 Shared reading
Alexander Belanger's guide is useful because it connects ordinary application decisions to the database behaviors they eventually expose: schemas, query shape, index ordering, connection pressure, autovacuum,...
My notes → Jul 29, 2026 Shared reading
Edward Yang offers a clarifying model for large-scale ML work: keep approachable PyTorch code as an executable reference, while specialized kernels and explicit forward-backward paths become the production...
My notes → Jul 29, 2026 Shared reading
Jovan Stojiljkovic runs the comparison that embedded-database conversations often skip: the same observability application, the same inexpensive machine, and disclosed load and read thresholds.
My notes → Jul 15, 2026 Shared reading
Alex Edwards turns the usual "use HTMX with server-rendered HTML" advice into a practical Go shape: a renderer that knows when to return a full page or a partial, embedded assets, explicit Vary: HX-Request handling, and...
My notes → Jul 15, 2026 Shared reading
Serge Zaitsev builds a compact tensor library in C from the representation upward: contiguous data, shapes and strides, broadcasting, kernels, SIMD, then enough layers and manual backward passes to train small models.
My notes → Jul 10, 2026 Shared reading
Gwern's proposal is useful because it asks a sharper question than "can agents write more code?" It asks what kind of software substrate would let code, proofs, docs, tests, and design intent scale together instead of...
My notes → Jul 10, 2026 Shared reading
Steve Krouse makes the case I still buy: learning to code is less a guaranteed career coupon now and more a literacy for thinking precisely with computers. The strongest angle is educational, not nostalgic.
My notes → Jul 10, 2026 Shared reading
This is a useful designer's notebook on software quality because it pushes quality out of the narrow "does it work" box and back into the felt experience of using a product.
My notes → Jul 10, 2026 Shared reading
This is a good practical explainer because it connects the usual "add an index" advice to the physical model underneath: pages, tuples, ctid, B-tree roots and leaves, and the way PostgreSQL can avoid dragging a query...
My notes → Jul 10, 2026 Shared reading
This is a good small-project reality check because it treats solo development as both a creative advantage and an operational liability.
My notes → Jul 6, 2026 Shared reading
This is more useful than another argument about vibe coding because it is a concrete trace: notes, mockups, data modeling, scaffolding, implementation transcripts, and maintenance around Diggit, a small git-history tool...
My notes → Jul 4, 2026 Shared reading
This is a generous first-principles walkthrough of software as layered abstraction: physical switches, bits, stored programs, compilers, operating systems, networks, and finally the applications people actually touch.
My notes → Jul 4, 2026 Shared reading
Velxio is interesting because it is not just an Arduino sketch runner in a browser. It tries to put the firmware, the circuit, and the instruments in the same feedback loop: AVR, RP2040, ESP32, RISC-V, and Raspberry Pi...
My notes → Jun 29, 2026 Shared reading
This piece is worth saving because it is less about whether AI can write code and more about what happens when the job shifts from making the thing to supervising the thing.
My notes → Jun 29, 2026 Shared reading
This is a wonderful tiny-hardware project: a Swift-flavored language environment for the Apple II, implemented through a bytecode VM instead of pretending full modern Swift can fit on a 1 MHz 6502 with tens of kilobytes...
My notes → May 2, 2026 Shared reading
A senior data engineer's decade of candid career lessons — job mobility, technology stacks, management
dynamics, and data-engineering-specific realities — delivered in a deliberately irreverent tone.
My notes → May 2, 2026 Shared reading
A full account of building Fame Boy — a working Game Boy emulator in F# — covering CPU, PPU, and APU
implementation, optimization decisions, and what the project revealed about how computers actually work.
My notes → May 2, 2026 Shared reading
The connector shape on USB-C says nothing about what the cable can actually do, which is why the
situation is so confusing. This guide cuts through it: why manufacturers ship slower "charge cables"
alongside capable...
My notes → Apr 29, 2026 Shared reading
This is one of the cleanest public pieces on agent design because it separates workflows from agents and keeps repeating the uncomfortable production lesson: start simple, add autonomy only when the task needs it, and...
My notes → Apr 28, 2026 Shared reading
Chip Huyen's agent writeup is useful as a systems map rather than a hype post. It treats agents as planning, tool use, memory, feedback, and environment design, which is the level where most production mistakes actually...
My notes → Apr 27, 2026 Shared reading
This is a good companion link for grounding the agent conversation in the reasoning-to-acting lineage instead of treating tool use as a brand-new product category.
My notes → Apr 27, 2026 Shared reading
A genuinely useful one-page reference for USB protocol versions, connector types, and what each combination actually supports. The kind of page I keep open in a tab for a week, then forget where I bookmarked it.
My notes → Apr 26, 2026 Shared reading
This is the DSPy link I would hand to someone who keeps saying "prompt engineering" when they really mean eval-driven optimization. The example is concrete, costed, and honest about train, validation, and holdout splits.
My notes → Apr 25, 2026 Shared reading
Part one is the practical on-ramp: a transcript-search use case, a RAG-ish starting point, and the moment DSPy becomes more than a nicer prompt template.
My notes → Apr 24, 2026 Shared reading
The thesis is deliberately provocative, which makes it useful. RAG is not simply dead, but the old chunk-embed-rerank default is under pressure from larger contexts, agentic retrieval loops, and document-native...
My notes → Apr 22, 2026 Shared reading
This is a good reminder that eval improvements do not always come from a larger model. Sometimes the gain comes from rewriting the task boundary so the model can actually see the job.
My notes → Apr 21, 2026 Shared reading
This is the runtime stack link for the gpt-oss moment: kernels from the Hub, MXFP4, tensor and expert parallelism, dynamic KV cache behavior, continuous batching, and faster loading.
My notes → Apr 21, 2026 Shared reading
Loose title, useful theme. Senior engineering is less about cleverness and more about judgment, communication, taste, scope control, and knowing where complexity will hurt later.
My notes → Apr 20, 2026 Shared reading
This is a docs link rather than an essay, but it belongs in the stream because it gives a practical path for experimenting with gpt-oss reinforcement learning workflows.
My notes → Apr 19, 2026 Shared reading
This forum post is useful because Agent Engine networking is exactly where cloud AI demos turn into platform engineering. Connectivity, controls, private access, and service boundaries are not side quests.
My notes → Apr 18, 2026 Shared reading
This is NVIDIA's enterprise agent argument for reasoning models: model size choices, test-time scaling, open deployment, and the operational need to turn reasoning on or off.
My notes → Apr 17, 2026 Shared reading
This follow-up is useful because it keeps the agent-model conversation tied to efficiency, not only capability. Smaller or better-routed reasoning models matter when an agent system has to run repeatedly, not just...
My notes → Apr 16, 2026 Shared reading
This is worth keeping because local and Windows-side inference are no longer edge cases for product teams. Hardware choices now shape who can prototype, test, and deploy models without waiting for a cloud GPU lane.
My notes → Apr 15, 2026 Shared reading
This belongs next to any local inference or training work because cross-platform ML is usually where clean notebooks meet real machines.
My notes → Apr 14, 2026 Shared reading
This is a strong local-inference link because it shows the gap between framework support and hardware-specific performance work. Apple Silicon can be serious, but the path often goes through kernels, not just...
My notes → Apr 13, 2026 Shared reading
This is reference-quality material for anyone trying to reason about accelerator performance without hand-waving. It explains GPUs as machines with memory, bandwidth, communication, and parallelism constraints.
My notes → Apr 12, 2026 Shared reading
This is a useful bridge for software developers who can build with LLM APIs but want to understand the math vocabulary underneath attention, embeddings, optimization, and probabilities.
My notes → Apr 11, 2026 Shared reading
This is the kind of long historical synthesis that helps engineers stop treating transformers as a sudden miracle. It traces the ideas through distributed representations, language modeling, attention, transformers, and...
My notes → Apr 10, 2026 Shared reading
This is a good visual refresher for the linear algebra that shows up everywhere in ML systems: vectors, matrices, transformations, projections, and the geometry behind model representations.
My notes → Apr 9, 2026 Shared reading
Evan Miller's statistics pages are useful because they speak to programmers who need decision math, not notation theater. This one is especially good as a quick reference for formulas that show up in experiments and...
My notes → Apr 8, 2026 Shared reading
This is worth keeping because good code reading starts before opening the first file. Git history can show what changed, where the pressure points are, and which parts of the system have been touched recently.
My notes → Apr 8, 2026 Shared reading
This is an older link, but the basics age well: understand the question, inspect the data, be skeptical of easy patterns, and communicate the analysis so someone can use it.
My notes → Apr 7, 2026 Shared reading
This is a softer link than the GPU and agent material, but it fits the site because visual thinking changes how technical ideas become explainable.
My notes → Apr 6, 2026 Shared reading
This is a beginner resource, but it is worth keeping because feature, label, target, and ground truth confusion still causes real project mistakes.
My notes → Apr 5, 2026 Shared reading
This is a practical reference link for the part of data science that happens before the model: selecting, filtering, grouping, reshaping, and checking tabular data.
My notes → Apr 4, 2026 Shared reading
PCA is one of those concepts that gets overused and under-explained. This is a useful reference for the intuition, terminology, and tradeoffs around dimensionality reduction.
My notes → Apr 3, 2026 Shared reading
This is one of the better engineering-process essays because it grounds motivation in visible progress. Break a large project into pieces that can produce real feedback, then sprint toward demos instead of disappearing...
My notes → Mar 31, 2026 Shared reading
This overlaps with the coding-assistant posts on the site, but it is still worth keeping as an outside practitioner account: small steps, screenshots, AGENTS.md, undo discipline, and refactoring agent-generated blobs.
My notes → Mar 29, 2026 Shared reading
This is the primary source behind the memory-optimization link in the saved list. Sakana's Neural Attention Memory Models are interesting because they try to learn what a transformer should remember or forget rather...
My notes → Mar 28, 2026 Shared reading
This is useful because the semantic layer keeps coming back under different names: metrics layer, business logic layer, AI query interface, governed data contract.
My notes → Mar 27, 2026 Shared reading
This is a good counterweight to traditional semantic-layer thinking because it asks whether AI-assisted query discovery changes where modeling work belongs.
My notes → Mar 26, 2026 Shared reading
This belongs in the queue because fine-tuning and inference decisions increasingly depend on numerical formats, not only model architecture.
My notes → Mar 25, 2026 Shared reading
This is a useful framing link because it shifts the work from clever prompts to the operational discipline of selecting, shaping, and evaluating context.
My notes → Mar 24, 2026 Shared reading
This is a Google-side reference for the embedding layer behind retrieval and context engineering. It is worth keeping because the site is already leaning into Vertex AI, Gemini, and production-grade AI systems, where...
My notes → Mar 23, 2026 Shared reading
This visual guide is useful because embeddings become easier to reason about when they stop being abstract vectors and start feeling geometric.
My notes → Mar 22, 2026 Shared reading
This is worth keeping because reasoning models change product expectations and evaluation shape. The important question is not only whether a model can think longer, but whether the extra compute improves the user's...
My notes → Mar 21, 2026 Shared reading
This is one of the foundational agent explainers: planning, memory, tool use, reflection, and the early patterns that still show up in current products.
My notes → Mar 20, 2026 Shared reading
This is the kind of platform link that turns model serving into an operations conversation. NIM, GKE, and operators are not glamorous pieces, but they are the difference between "we can run a model" and "we can run the...
My notes → Mar 19, 2026 Shared reading
This is useful because it connects agent adoption to inference architecture. Agents do not make one call; they fan out across planning, retrieval, tool use, retries, and evaluation, which changes the serving math...
My notes → Mar 18, 2026 Shared reading
This is a durable architecture reference because it gives teams a simple way to choose the right level of diagram: context, containers, components, and code. Good diagrams reduce argument cost.
My notes → Mar 17, 2026 Shared reading
This is worth keeping because bronze, silver, and gold layers remain a common language for data platform design. Even when the exact stack changes, the idea of progressive refinement is still useful.
My notes → Mar 16, 2026 Shared reading
This is a classic reference for understanding PostgreSQL below the SQL surface. Query processing, storage, concurrency, and transaction behavior become much less mysterious when the internals are visible.
My notes → Mar 15, 2026 Shared reading
This is a small but useful data-team link because SQL style is a collaboration tool. A query that is easy to review, modify, and debug is a data product maintenance win.
My notes → Mar 14, 2026 Shared reading
This belongs in the queue because database performance is often where application assumptions meet physical reality. Indexes, query plans, and access patterns are product concerns when latency becomes user-visible.
My notes → Mar 13, 2026 Shared reading
This is useful because data modeling is still one of the quiet foundations of analytics, ML, and application design. Good models make downstream work boring in the best way.
My notes → Mar 12, 2026 Shared reading
This is a good systems explanation for software developers who use databases every day but have not built a mental model of parsing, planning, storage, indexes, and execution.
My notes → Mar 10, 2026 Shared reading
This is an evergreen frontend architecture link because URL state is not just routing trivia. It affects sharing, refresh behavior, deep links, browser history, analytics, support, and whether a product feels native to...
My notes → Mar 9, 2026 Shared reading
This older workflow/state-machine link is worth keeping because the lesson keeps repeating: protocols, product flows, and background jobs become easier to reason about when states and transitions are explicit.
My notes → Mar 8, 2026 Shared reading
This is a useful architecture link because CQRS is powerful and easy to overapply. Separating reads from writes can clarify a system, but it can also introduce synchronization, consistency, and mental overhead.
My notes → Mar 7, 2026 Shared reading
This belongs in the queue because "event-driven" can mean several different things, and teams get into trouble when they use the same phrase for different architectures.
My notes → Mar 6, 2026 Shared reading
This is a broad reference, but it is still worth keeping because system design vocabulary helps developers talk across product, infrastructure, and data boundaries.
My notes → Mar 5, 2026 Shared reading
This is one of the React links that still earns a place because it explains the runtime model instead of another surface API. Understanding React as a way to describe, schedule, and reconcile UI work ages better than...
My notes → Mar 4, 2026 Shared reading
This is an evergreen frontend architecture piece because it names the hard parts of UI work: state, latency, consistency, environment, and synchronization. Those problems do not disappear when the framework changes.
My notes → Mar 3, 2026 Shared reading
This is a small but practical product-engineering link. Browser-native form behavior is often ignored while teams rebuild validation, accessibility, and UX affordances from scratch.
My notes → Mar 2, 2026 Shared reading
This is worth keeping because it questions the default assumption that app-like experiences require a full client-side framework. That is a healthy pressure test for product architecture.
My notes → Mar 1, 2026 Shared reading
This is a useful frontend architecture link because it pushes beyond taste and asks what tradeoffs teams inherit when React becomes the default answer.
My notes →