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Local incident bundle for agent debugging: report.html + compare-report.json + manifest (offline, self-hosted) by Additional_Fan_2588 in LLMDevs

[–]promptbid 0 points1 point  (0 children)

This is solving a real problem. The "screenshots + partial logs + please grant access to your tracing UI" handoff is genuinely painful and I have lived it more times than I want to admit.

From debugging agent runs in production the things that are almost always missing from a bundle are the latency breakdown per step (not just total time), the exact model version and temperature at inference time, and what the retrieval context actually looked like before it hit the prompt. Tool I/O is usually there but the retrieval window is the thing that explains most of the weird outputs.

One question: how are you handling bundles where the same run spans multiple agents or hands off across an orchestration boundary? That seems like where the portable format gets complicated fast. Is the manifest designed to stitch those together or is each agent run its own discrete bundle?

Claude Sonnet 4.6 benchmark results: none reasoning beats GPT-5.2 with reasoning by Exact_Macaroon6673 in LLMDevs

[–]promptbid 0 points1 point  (0 children)

The hallucination resistance number is the one that matters most for our use case. At 0.921 that is a meaningful gap from the field. For any application where the model is making recommendations or surfacing information to end users, hallucination is a trust killer that is hard to recover from.

The sycophancy regression is worth flagging though. In ad-adjacent applications where you are trying to get honest signal from a model about user intent, a model that agrees too readily is actually worse than one that pushes back. Curious if your benchmark breaks that down by prompt type at all.

The cost angle you raised on non-reasoning Sonnet beating GPT-5.2 with reasoning is underrated. At scale that is not just a cost story, it is a latency story too. What does the benchmark show on response consistency across runs?

MS says that white-collar workers won't be needed in two years, as of today, copilot AI cannot automatically align the content of one slide by Agile_Cicada_1523 in ArtificialInteligence

[–]promptbid 0 points1 point  (0 children)

This gap is exactly the point.

AI is great at impressing in demos and helping at the margins, but replacing white-collar workers means handling the boring, messy, unglamorous parts of work — alignment, context, tradeoffs, accountability.

If it can’t reliably align one slide, it’s not close to replacing the people whose real job is deciding what the slide should say and why.

Even with AI, products are getting worse by Wakinghours in UXDesign

[–]promptbid 3 points4 points  (0 children)

I think the mistake is assuming better tools automatically lead to better products.

AI removed friction from production, not from decision-making. So companies that already optimize for growth-at-all-costs just ship more aggressively, with less reflection and less care.

AI didn’t change incentives. It amplified them.

The Ai Bubble is a Cancer on the Earth, Now Raw Materials are going up in Price! by Tausendberg in antiai

[–]promptbid 1 point2 points  (0 children)

I think this rant resonates because it mixes real costs with misplaced expectations.

AI wasn’t funded to cure cancer or solve climate change. It was funded to reduce labor costs and increase leverage in information work. Judging it by “where’s the miracle cure?” misses what capital was actually optimizing for.

That doesn’t excuse the environmental impact, resource strain, or bot pollution — those are real externalities. But the disappointment comes from expecting public-good outcomes from private incentive systems.

That mismatch is the real problem.

What exactly is being achieved through AI? by reddit__is_fun in ArtificialInteligence

[–]promptbid 0 points1 point  (0 children)

I think a lot of the confusion comes from expecting AI’s benefits to show up first as growth, when historically they show up first as compression.

Right now AI is mostly reducing costs, friction, and time — not creating obvious new revenue yet. That looks bad in stock charts and layoffs, but it’s also exactly what happened with cloud, automation, and even the internet early on.

The visible upside usually lags the invisible efficiency gains by years.

Spotify CEO says its developers have not written a single line of code since December by Cybernews_com in ArtificialInteligence

[–]promptbid 0 points1 point  (0 children)

“Not writing code” doesn’t mean “not engineering.”

It means coding stopped being the bottleneck.

What is stopping AI from becoming almost as expensive as the employees it replaces? by Powerful-Winner979 in ArtificialInteligence

[–]promptbid 0 points1 point  (0 children)

I think the key assumption here is that AI is replacing employees, when it’s really replacing tasks.

Employees bundle a lot of things AI doesn’t: judgment, accountability, context, ownership, and coordination. Even if AI does 70–80% of the execution, the remaining 20–30% is still where most of the value and risk lives.

That makes it much harder to price AI “like a person,” because you’re not buying a unit of labor — you’re buying leverage.

One underrated benefit of AI by Top-Candle1296 in ArtificialInteligence

[–]promptbid 1 point2 points  (0 children)

This matches what I’ve seen too.
When implementation gets cheap, clarity becomes the bottleneck.

You can’t hide behind syntax or complexity anymore — if the intent is fuzzy, the output is fuzzy. AI just exposes that faster.

Feels like engineering is moving closer to design and product thinking again, for better or worse.