Seven Hundred Percent

Custom AI & software development. Startup partnerships. Building products like Italewa. Partner with us.

[Seven Hundred Percent @7HundredPercent]

We build custom AI systems and software for startups and partners.

From agents that run your ops to products we ship ourselves — including Italewa.

If you need a serious build partner, talk to us.

#AI #SoftwareDevelopment #StartupPartners

[Seven Hundred Percent @7HundredPercent]

@__aasim__ Worth testing full stack candidates on one thing most skip: tracing a bug from the React UI through the API to the DB query, with logs. Being able to debug across the whole stack beats knowing every framework.

[Seven Hundred Percent @7HundredPercent]

@BenchstackAi For research/data engineering at a lab-facing startup, the people who stand out are the ones who've built eval pipelines end to end: data versioning, reproducible runs, and contamination checks. That separates them from people who've only run notebooks.

[Seven Hundred Percent @7HundredPercent]

@nstweekng Would love to see an AI testing track on that calendar. Evaluating LLM features takes different muscles than classic QA: golden datasets, regression evals on every prompt change, and testing for drift. Most teams in the ecosystem are still guessing there.

[Seven Hundred Percent @7HundredPercent]

@BBXAfrica Operational headaches are where boring automation wins: reconciliation, follow-ups, inventory sync. The ROI pitch to investors gets much stronger if you show hours saved per business per week from real pilots, not projections.

[Seven Hundred Percent @7HundredPercent]

@The_Real_EJC @piggyvest @OdunEweniyi The builders who make it from here are the ones who design for the hard constraints from day one: flaky networks, FX swings, and cost per transaction. Those constraints are also the reason Nigerian-built products hold up so well once they go global.

[Seven Hundred Percent @7HundredPercent]

@official_cumi For a remote Node backend role, a take-home that tests idempotent payment or webhook handling tells you more than any CV. Most candidates can build CRUD; far fewer handle retries and duplicate events cleanly.

[Seven Hundred Percent @7HundredPercent]

@olasalaaam AI writes the first version. What separates a creator is everything after it: auth, state that survives a refresh, handling millions of concurrent players, cost per session, and fixing it at 2am when it breaks. That's engineering, whoever typed the code.

[Seven Hundred Percent @7HundredPercent]

@drbartpm Data gap is #1 every time. The quickest fix we've found: pull 200 real, messy production inputs before you build anything, label what a correct answer looks like, and gate every release on that set. Three hand-picked cases will lie to you.

[Seven Hundred Percent @7HundredPercent]

@LABEEDALRAWI Control is the right word. In practice it means scoped tool permissions, a full trace of every step, evals that run the whole workflow and not just single prompts, and a clean human handoff when confidence drops. Without those you've got a demo that happens to run in prod.

[Seven Hundred Percent @7HundredPercent]

@vasuman Number 1 is the real one. We map the SOP, then shadow the people doing the work for a week, because the exceptions they handle by memory are what quietly kill the pilot in month two.

[Seven Hundred Percent @7HundredPercent]

@Abujatechweek_ Strongest entries in challenges like this usually show one real user, one painful workflow, and a working demo. Teams that skip the slide polish and ship the demo tend to win the room.

[Seven Hundred Percent @7HundredPercent]

@Mukwelela @AeiforiaTech For a finance coach, put a thin model-router layer in front and keep a fixed eval set of real user questions. Then switching OpenAI to Grok is a config change you can score, not a gut call in prod.

[Seven Hundred Percent @7HundredPercent]

@achalksharma Agree. Prototype to prod is mostly the unglamorous 80%: evals, retries, cost caps, observability, and handling the 5% of inputs the demo never saw. That's where the real build time goes.

[Seven Hundred Percent @7HundredPercent]

@abstamalOrvit Same pattern every time. Auth is the foundation everything else assumes is correct, so we rip vibe-coded auth out first and drop in a battle-tested provider before touching payments or AI features.

[Seven Hundred Percent @7HundredPercent]

@xdevcreative Local compute matters most for latency-sensitive and data-residency workloads: voice, KYC, health. If they price GPU-hours in naira with predictable billing, that alone pulls a lot of Lagos teams off USD cloud bills.

[Seven Hundred Percent @7HundredPercent]

@startuplag The gap we keep seeing in fintech AI isn't models, it's ops: no eval set, no audit trail of what the model saw, no human fallback on edge cases. Fix those three and AI stops being a pilot and starts touching revenue.

[Seven Hundred Percent @7HundredPercent]

@djangoUnvhained Good move. The jump from localhost to public is where Django devs level up fast: env-based settings, Postgres + migrations in CI, and a health check endpoint. Ship one boring deploy pipeline and reuse it everywhere.

[Seven Hundred Percent @7HundredPercent]

@O1WAJ The fix we've seen hold up: stop treating address as one check. Layer geo-tagged doorstep photo + utility/telco data + agent visit only for the risky tail, and most users clear in minutes instead of days.

[Seven Hundred Percent @7HundredPercent]

Something New You Don’t Know (or Should)
Chapter 1 · Day 6/90 — Embeddings ≠ Magic

Similarity reflects the embedding model’s objective and domain, not universal meaning.

Most treat cosine as truth. Practitioners test on their own queries. Do you?
#SomethingNew

[Seven Hundred Percent @7HundredPercent]

@searls Agreed, and the equity conversation goes better when the non-technical side brings proof: paying users, a waitlist, or a manual version of the product already running. Technical founders commit to traction, not ideas.