We're building a personalized language-learning app inspired by our own experiences and frustrations with the one-size-fits-all approach of traditional apps. Our goal is to offer customized and adaptive learning paths, powered by user data and tailored to individual interests, goals, and motivations.
I won t go into full pitch mode just yet, but we d love to start a discussion around this question:
What s your biggest pain point or frustration with current language-learning apps?
When your coding agent finishes a change and you ask it to look the change over, what is in the reviewer's window? If the answer is the whole session, the review is the author re-reading with its own notes in front of it: every file it opened, every dead end, and the moment it declared the change done. It knows why each line is there. A reviewer should not.
We wanted evidence that moving the review out of that window matters when the model stays the same. One small controlled study did exactly that: thirty artifacts with five planted errors each, one model, four review setups. The fresh window given nothing but the artifact caught the most, F1 28.6% against 24.6% for the full session and 40% of the critical errors against 29%, while a second pass inside the same session came last. Two agent vendors already build it that way: one CLI's guide puts it as a fresh context helping because the model "won't be biased toward code it just wrote", and the other vendor's team says one model does both jobs and was trained separately for each, with the reviewer running on the pull request.
Our own version reviews articles using a cold reviewer agent, handed that day's source material as ground truth. Each piece comes back with somewhere between one and twenty-three items we have to fix, usually four to six, and the hard ones were caught against the pasted sources.
If your agent reviews its own change, what does the reviewer get to see: the diff alone, the diff plus the task, or the whole transcript? And who has put two of those side by side on the same change?
Receipt parsing sounded like a solved problem until I actually built it.
Thermal paper fades in 60-90 days. Retailers print GST invoices in at least a dozen layouts. Some put the warranty period in the footer, some in the line item, some nowhere at all. And the one field you need most, the purchase date, appears as 07/03/25, 7-Mar-2025, or 070325 depending on the billing software.
Our first parser got 61% field accuracy. Shipping that would have meant users correcting the AI more often than trusting it, which kills the product.
joining a new community always comes with a bit of a learning curve. I'm trying to figure out how to best balance scrolling through new products leaving useful feedback and supporting cool launches without getting overwhelmed.
For those who have been active here for a while what did your daily routine look like when you first started?
Any simple tips for jumping into discussions without overthinking every single comment?
Hello! Quick question: do you use TikTok to promote your business? If yes, do you consider TikTok as a game-changer in your social media marketing? If no, do you plan to post content there?
