farkin88

Thanks for sharing. That's really cool.

X11's SECURITY extension was its long-forgotten stab at sandboxing: flip a bit and every client is either trusted or untrusted. It does kill trivial key-logging, but it also breaks the clipboard, disables GLX and makes various apps fall over, leaving the desktop unusable while Firefox somehow works just fine. A cool reminder that X11 could've had proper sandboxing 25 years ago, but the UX cost sank it and Wayland is the lifeboat now.

Anthropic still relies on quotas (5-hour rolling + weekly coming at the end of the month) so the next step is dynamic per-token pricing. But, even with transparent off-peak rates only batch jobs will shift and history suggests variable pricing usually smooths rather than sharpens peaks. The long-term fix stays the same: route 100 MB PDFs, repo refactors and other whale jobs through retrieval or analysis pipelines and keep the flagship chat model for real-time conversation.

Fair point about process bloat. Just to clarify: I'm not an ADR expert nor do I personally know Harmel-Law. Just deriving insights from his blog post that I linked to earlier because I thought it was interesting.

But, I think you might be picturing ADRs as heavier than they need to be. Most changes seem like routine updates ("We decided Postgres" → "We're migrating to Aurora") that go through normal code review. The big architectural shifts that would trigger Security/Compliance review probably should anyway, ADR or not.

The key insight is that ADRs document decisions, they don't create them. If a change is big enough to need committee review, it likely needs that scrutiny regardless of whether there's documentation.

The alternative, undocumented drift, often creates much longer delays when people rediscover decisions during incidents. Curious to hear from folks who've actually implemented this at scale, though.

The prod-readiness concerns are fair, but mcp-use fills a real gap in the MCP stack: orchestration across many servers with far less boilerplate than the official SDK. Even if the agent is as another commenter fairly pointed out, just a LangChain wrapper, the six-line setup and server manager are a nice DX upgrade.

What’s still missing is structured error semantics. Right now, when a tool explodes the LLM only sees a raw stack trace, so it can’t decide whether to retry or bail. Today, I saw another Show HN post on MCP here: https://news.ycombinator.com/item?id=44778968 which converts exceptions into a small JSON envelope (error, details, isRetryable, etc.) that agents can interpret. Dropping that into mcp-use’s client loop would give you observability + smarter retries for almost free.

I might be missing some important nuances here between client side and server side MCP error handling, which I’ll leave to the authors to discuss. Just thought there might be a good open source collab opportunity here.

That robustness-vs-throughput trade-off is such a staple of PDF parsing. My guess is that the new path is slower because the recovery scan now always walks the whole byte range and has to inflate any object streams it meets before it can trust the offsets even when the first startxref would have been fine.

The 10k-file test set sounds great for confidence-building. Are the failures clustering around certain producer apps like Word, InDesign, scanners, etc.? Or is it just long-tail randomness?

Reading the PR, I like the recovery-first mindset. If the common real-world case is that offsets lie, treating salvage as the default is arguably the most spec-conformant thing you can do. Slow-and-correct beats fast-and-brittle for PDFs any day.

There are so many external links, it's easy to get lost in this article. Look under content for the section titled "1. A Thinking and Recording Tool: Decision Records." It's under "The Four Supporting Elements." Here's a direct link if it's easier https://martinfowler.com/articles/scaling-architecture-conve... (Just search on that page for "The Four Supporting Elements)

There Harmel-Law defines ADRs as "lightweight documents, frequently stored in source code repositories alongside the artefacts they describe." He also provides a handy "Elements of an ADR" table. Let me know if you're still having problems finding it.

Great rundown. One thing you didn't mention that I thought was interesting to note is incremental-save chains: the first startxref offset is fine, but the /Prev links that Acrobat appends on successive edits may point a few bytes short of the next xref. Most viewers (PDF.js, MuPDF, even Adobe Reader in "repair" mode) fall back to a brute-force scan for obj tokens and reconstruct a fresh table so they work fine while a spec-accurate parser explodes. Building a similar salvage path is pretty much necessary if you want to work with real-world documents that have been edited multiple times by different applications.

Solid advice on clarity and editing. The only gap is what happens after the doc is approved? Without upkeep it decays into "design archaeology." A few years ago, Andrew Harmel-Law wrote about an interesting approach to scaling architecture conversationally, which includes lightweight Architecture Decision Records (ADRs) as one tool that could help here. ADRs live beside the code (adr/001-use-postgres.md) and capture context, decision, and status in a format short enough to, I think, revisit in every PR and easy to supersede when reality changes so the original rationale stays searchable months later.

Here’s a link to Harmel-Laws’post if anyone's interested: https://martinfowler.com/articles/scaling-architecture-conve...

For sure. Feel free to copy/paste it. Great blog by the way. Will keep any eye out more of your posts.

Type safety for API calls is huge. I haven't used ts-rest but the compile-time validation approach sounds solid. Way better than runtime surprises. How's the experience in practice? Do you find the schema definition overhead worth it or does it feel heavy for simpler endpoints?

What a dual-publishing nightmare. Someone had to break the stalemate first. 90% size reduction is solid even if Node bundle size isn't as critical. The streams thing sounds messy, though. Two incompatible streaming standards in the same runtime is bound to create headaches.

Ky is definitely one of the libraries moving in that direction. Good adoption based on those download numbers, but I think the ecosystem is still a bit fragmented. You've got ky, ofetch, wretch, etc. all solving similar problems. But yeah, ky is probably the strongest contender right now, in my opinion.

Yeah, that's the classic bundle size vs DX trade-off. Fetch definitely requires more boilerplate. The manual response.ok check and double await is annoying. For Lambda where I'm optimizing for cold starts, I'll deal with it, but for regular app dev where bundle size matters less, axios's cleaner API probably wins for me.

Undici is solid. Being the engine behind Node's fetch is huge. The performance gains are real and having it baked into core means no more dependency debates. Plus, it's got some great advanced features (connection pooling, streams) if you need to drop down from the fetch API. Best of both worlds.

I think that's the sweet spot. Native fetch performance with axios-style conveniences. Some libraries are moving in that direction, but nothing's really nailed it yet. The challenge is probably keeping it lightweight while still solving the evaluating 5 retry packages problem.

Interesting to get a library author's perspective. To be fair, you guys had to deal with the whole ecosystem shift: dual package hazards, CJS/ESM compatibility hell, tooling changes, etc so I can see how ESM would be the bigger story from your perspective.

Totally get that! I think it depends on your context. For Lambda where every KB and millisecond counts, native fetch wins, but for a full app where you need robust HTTP handling, the axios plugin ecosystem was honestly pretty nice. The fragmentation with fetch libraries is real. You end up evaluating 5 different retry packages instead of just grabbing axios-retry.

Right?! I think a lot of devs got stuck in the axios habit from before Node 18 when fetch wasn't built-in. Plus axios has that batteries included feel with interceptors, auto-JSON parsing, etc. But for most use cases, native fetch + a few lines of wrapper code beats dragging in a whole dependency.

Big takeaway for me: the win isn’t better prompts, it’s semantic guarantees. By proving at the bytecode level that the pixel loop is side-effect-free, you can safely split it into long-lived workers and use an order-preserving queue. It's an aggressive transform copilots won’t attempt because they can’t verify invariants. That difference in guarantees (deterministic analysis vs. probabilistic suggestion) explains the 2× gap more than anything else.

I've been burnt by performance gates on GitHub Actions. One random timing spike and the whole PR turns red. The coefficient of variation math here nails why: GitHub Actions shows a 2.66% CV, which means a 2% performance gate gives you a 45% false positive rate (basically every other run flags a fake regression). No wonder developers stop trusting the check. In my experience the only way to make benchmarks actionable is to run them on deterministic bare-metal runners, whether CodSpeed's or something you host yourself.

Even though tokens are getting cheaper, I think the real killer of "unlimited" LLM plans isn't token costs themselves, it's the shape of the usage curve that's unsustainable. These products see a Zipf-like distribution: thousands of casual users nibble a few-hundred tokens a day while a tiny group of power automations devour tens of millions. Flat pricing works fine until one of those whales drops a repo-wide refactor or a 100 MB PDF into chat and instantly torpedoes the margin. Unless vendors turn those extreme loops into cheaper, purpose-built primitives (search, static analyzers, local quantized models, etc.), every "all-you-can-eat" AI subscription is just a slow-motion implosion waiting for its next whale.

The killer upgrade here isn’t ESM. It’s Node baking fetch + AbortController into core. Dropping axios/node-fetch trimmed my Lambda bundle and shaved about 100 ms off cold-start latency. If you’re still npm i axios out of habit, 2025 Node is your cue to drop the training wheels.

I really like the tiny-lib approach. One thing that would make it even more useful in larger systems: OpenTelemetry hooks is if each call to formatMCPError() could optionally:

start/finish a span (or attach events to the caller’s span), propagate requestId as the span ID / traceparent, and export errorType, isRetryable, etc. as span attributes.

That way every tool failure would show up in our OTEL dashboards with perfect cross-service correlation. We could drill from an agent retry loop straight to the failing GitHub API call (or user-cancel event) in one click.

I think OTEL-JS adds a few kB (need to double check that) and can be opt-in via a withTracing: true flag, so the “tiny by default” goal stays intact. What do you think?