Most "AI research assistant" demos are impressive-looking and unfalsifiable — you can't tell if the summary is accurate without redoing the research yourself. This one is built the opposite way: every signal below is cited back to a real, public, checkable source, and the model's instructions explicitly say to admit when the facts don't support a claim, rather than force one.
Universal memory layer for AI agents, 60K+ stars, 100+ contributors. Three momentum signals worth opening with:
The three signals above are real, pulled live via the GitHub and Hacker News APIs. The brief text was written by hand to match exactly what the system prompt in brief_generator.py is instructed to produce when it's only allowed to cite those signals — I didn't have an API key available when building this to generate it live. Run the script yourself with your own ANTHROPIC_API_KEY against any repo and you'll get an actual model-generated brief, grounded the same way.
Full script: brief_generator.py — data-gathering half needs no key, synthesis half needs your own Anthropic key.
# the rule that keeps this from being a hallucination machine:
SYSTEM_PROMPT = """You write short, specific pre-call account briefs.
Rules:
- Use ONLY the facts given. Never invent a metric, a pain point, or a quote.
- If the facts don't clearly support a signal, say "no clear signal here"
instead of forcing one.
- 3 discovery-call talking points that reference those specific signals —
never generic sales questions."""
This is the "AI-assisted deal support" half of GTM engineering — using real signal to prep a human for a conversation, not just finding the signal in the first place. Same discipline as the signal-scoring demo and the mem0 audit: real data, cited sources, and an explicit instruction to admit uncertainty instead of faking confidence.
Or write directly: ai.adityachouhan@gmail.com