StackAI @HelloStackAI

🚀 Democratizing AI training data. High-quality synthetic training data for researchers & startups. Fine-tune LLMs without hyperscaler budgets. 🧪 stackai.app Canada 🇨🇦 Joined February 2026

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StackAI @HelloStackAI

2 months ago

A dataset can look diverse and still be repetitive. If 1,000 rows are really 3 templates with different names, the model learns template completion, not task competence. Count scenario families, not just rows. Wording variation is cheap. Situation variation is what generalizes.

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StackAI @HelloStackAI

2 months ago

A single eval score hides too much. 82% overall can still mean: - 97% on short clean prompts - 41% on long messy ones - 28% when fields conflict Track performance by failure slice, or the average will flatter the system you wish you had.

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StackAI @HelloStackAI

2 months ago

@ProtonSupport Got in this time. Thank you for the support, and keep up the great work :) Upvoted someone else asking for same """ Select all emalis matching a search """

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StackAI @HelloStackAI

2 months ago

@ProtonMail customer from the start here! Just tried to make a feature request and @uservoiceinc doens't seem to accept your alias emails... (I made sure it wasn't a password issue) Might want to have a chat with them.

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StackAI @HelloStackAI

2 months ago

@elonmusk I love how recent the content is. Not some documentary from last year. Literally days ago 🚀

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StackAI @HelloStackAI

2 months ago

@ProtonSupport Thanks for the reply! ProtonMail web. If I search for a keyword in email subject and have so many results it's paginated, it would be great if I could select all results across all pages (for a bulk move or bulk delete for example). Re UserVoice did you try through ProtonVPN?

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StackAI @HelloStackAI

2 months ago

@Jason I'm in

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StackAI @HelloStackAI

2 months ago

A lot of ML teams are not blocked on training code. They are blocked on turning domain knowledge into a dataset that is structured enough to use, broad enough to generalize, and clean enough to trust. That data design step is where a lot of the real work lives.

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StackAI @HelloStackAI

2 months ago

If you generate 10,000 synthetic examples, the first question is not “how many?” It is: - are they consistent - are they realistic - do they cover the edge cases you care about - would you actually trust them in training Volume is easy. Trust is harder.

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StackAI @HelloStackAI

2 months ago

Hard negatives do more than catch model mistakes. They define the boundary of the task. If you only show a model good examples, it learns what to do. If you also show realistic wrong examples, it learns what not to do. That is usually where reliability starts improving.

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StackAI @HelloStackAI

2 months ago

One common eval mistake is testing only the happy path. If your users will send messy, ambiguous, or incomplete inputs, your eval set should too. A clean benchmark can make a brittle system look better than it is.

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StackAI @HelloStackAI

2 months ago

A lot of synthetic data projects go wrong before generation even starts. If you cannot describe the fields, constraints, and failure cases clearly, the model will improvise them for you. Good synthetic data starts with a tight schema, not a bigger prompt.

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StackAI @HelloStackAI

2 months ago

When a team asks for more training data, I usually ask one question first: More data for which failure mode? Over-refusal, hallucinated fields, format drift, weak rankings, missed edge cases. If you cannot name the failure, you cannot generate the fix.

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StackAI @HelloStackAI

3 months ago

@stableAPY That's the pattern. Nobody documents the filtering pipeline because it feels like infrastructure, not the interesting part. But that's where the actual quality delta comes from.

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StackAI @HelloStackAI

3 months ago

@Link_Swarm 100% in integration mode now. For us the biggest unlock has been connecting existing data sources to LLMs without rebuilding pipelines from scratch. Teams ship in hours instead of weeks. What workflow have you found most worth automating first?

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StackAI @HelloStackAI

3 months ago

@saen_dev @marcel_butucea Completely this. Benchmark hallucination rates on closed-domain synthetic data are basically marketing. The failure mode is always at distribution boundary: regulatory language not in training, ambiguous acronyms, edge-case entity resolution. That's where data quality matters.

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StackAI @HelloStackAI

3 months ago

@awagents The dirty secret: most fine-tuning projects spend 80% of the time on data and 20% on actual training. The training part is almost the easy part once you have clean data.

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StackAI @HelloStackAI

3 months ago

@mentalgeorge The quality of that upstream spend (especially synthetic data quality) determines whether the final training run is worth doing at all. Garbage in, garbage out, no matter the scale.

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StackAI @HelloStackAI

3 months ago

@theaiteen @akshay_pachaar Nine times out of ten this is a data problem. Either the examples are inconsistent, diversity is too low, or the eval set overlaps with training. Which of those does your pipeline catch before training starts?