laalshaitaan

5 ms·

31.
by laalshaitaan 7mo ago
The MoE point matters here ie sparse activation means you're not reading all 2TB per forward pass, but the access pattern flips from sequential to random which is exactly the worst case for NVMe. Been thinking about this a lot for agen
32.
by laalshaitaan 7mo ago
We've seen this exact pattern. Most devtools assume a human will eventually log in and contextualize the data. When the 'user' is an agent, you need the surface to be machine-readable by default, not as an afterthought. The a
33.
by laalshaitaan 7mo ago
4.4 tok/s with reliable structured output is a solid local benchmark altho the question is whether SSD streaming introduces per-token latency variance that messes up tool call parsing downstream. The gap between 400 GB/s unified m
34.
by laalshaitaan 7mo ago
we've started to document any a/b decision we take in terms of tech and store in our engineering internal docs! have gone back to it once in a whilw but that usually helps keep us grounded
35.
by laalshaitaan 7mo ago
IMO the under-discussed risk here is that sites will start serving different content to verified crawlers vs real users. You're already seeing it with known search bots getting sanitized views. If your agent's context comes from a
36.
by laalshaitaan 7mo ago
Hard limits are a good first layer but they don't tell you why the agent is looping. Retrying because it's confused, retrying because a dependency is flaky, and genuine planning loops are three different problems with different fi
37.
by laalshaitaan 7mo ago
The BCG framing makes it sound like a cognitive load problem but I think it is more unreliability fatigue. When your AI does 8 things right and then confidently does the 9th wrong, you spend mental energy second-guessing everything. Supervi
38.
by laalshaitaan 7mo ago
Same mental model problem comes up in AI agent observability. Two conversation flows can produce identical user outcomes and look totally different at the message level, or vice versa. The normalization step that actually captures 'did
39.
by laalshaitaan 7mo ago
The latency point matters more than it looks imo like the GPU work isn't just async CPU work at a different speed, the cost model is completely different. In LLM inference, the hard scheduling problem is batching non-uniform requests w
40.
by laalshaitaan 8mo ago
Was using this in our prod microservices, has been helping us with value instantly! Really in for the vision of AI SREs
41.
by laalshaitaan 8mo ago
Lol the joke works because it's halfway serious. Agents already choose tools, vote with API calls, and quietly churn off platforms that create friction. "What do agents want" is actually a real product question and imo most t
42.
by laalshaitaan 8mo ago
The pattern is always the same: product team has a new AI feature, someone says put it everywhere, nobody asks users if they want it, and then power users revolt while the feature languishes unused anyway. The right move was opt-in, but tha
43.
by laalshaitaan 8mo ago
The "trust our logs" problem is real ie regulators and security teams don't care about your dashboard. Curious about the semantic layer though: once you can verify a log is intact, the next hard question is why the agent made
44.
by laalshaitaan 8mo ago
The "central server is usually right" framing holds until you hit cases where ordering matters and latency is non-negotiable. We ran into this building conversation stores for AI agents where concurrent turns need to converge with
45.
by laalshaitaan 8mo ago
Building your own language to build your own engine to build your own game is what most orgs would flag as scope creep. But that's exactly what lets Wouter push things nobody else can lol he controls every constraint all the way down.
46.
by laalshaitaan 8mo ago
The test harness point is spot on but there's a gap worth naming: the failure modes you write evals for aren't the ones that cause users to churn. Prod conversations have a whole category where the agent doesn't error, it jus
47.
by laalshaitaan 8mo ago
The enterprise angle makes more sense than consumer. Regulated industries and gov orgs need auditable device stacks, and Pixel being the only viable GrapheneOS hardware was always a fragile dependency for a security-first product. The real
48.
by laalshaitaan 8mo ago
Stylometry is just the most legible version of this. The harder-to-defend surface: posting time patterns, topic clusters, cross-platform phrase matching, interaction graphs. LLMs synthesize weak signals at scale in a way no single analyst c
49.
by laalshaitaan 8mo ago
The merge conflict is the symptom. The root problem is parallel agents have no coordination primitives before edits happen. The MCP server angle is the more interesting long-term bet here because it moves conflict avoidance earlier in the w
50.
by laalshaitaan 8mo ago
The carrying cost almost never gets charged to the builder. Team pays it later in surface area, failure modes, and onboarding time. The only fix I've seen work: write down the two options you didn't take and a concrete trigger for
51.
by laalshaitaan 8mo ago
The best point in this thread: "intuition as evals." That's actually the crux. PMs have context about what the product should do that engineers often don't, and that intuition IS an eval. The problem is it doesn't s
52.
by laalshaitaan 8mo ago
Formal verification gets you to deploy with confidence but it's still a snapshot. What happens when real-world inputs drift from what you tested against? The subtler problem is runtime behavioral drift: an agent that's technically
53.
by laalshaitaan 8mo ago
The worst part isn't that chatbots are bad at their job. It's that teams shipping them genuinely don't know how bad they are. Nobody's reading the conversations, so nobody catches users hitting dead ends, rage-clicking,
54.
by laalshaitaan 8mo ago
The full-session evaluation framing is the right call - most teams don't realize the failure happened in turn 2 until they've spent 3 hours blaming the model. One thing worth thinking about as you grow: connecting caught regressio
55.
by laalshaitaan 8mo ago
lol This gets worse with AI search. If Google can't figure out canonical source from a GitHub repo linking directly to the official site, LLMs definitely can't. And once an AI overview bakes the fake site into its knowledge graph,
56.
by laalshaitaan 8mo ago
The IC vs EM debate always skips the third option: neither. Start something. You end up writing code, managing people, doing sales, and shipping product in the same week. Way harder, but you never have to wonder if the ladder is worth climb
57.
by laalshaitaan 8mo ago
The conversation around measuring task duration misses what most product teams actually care about: not can the agent complete a long autonomous run, but are users getting value? The signal that matters for shipped products is different, wh
58.
by laalshaitaan 8mo ago
to me i solely evaluate on the way they write prompts and how elaborate they are in terms of explaining it and the patience when the model messed up
59.
Ask HN: Do you find Analytics dashboards cluttered?
2 points by laalshaitaan 8mo ago|0 comments
60.
Ask HN: How do you understand what users want when your interface is a chat?
3 points by laalshaitaan 8mo ago|2 comments

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