For most people, the law is unreadable. Knowing your rights in Nigeria means either wading through statutes written for lawyers or trusting a stranger online who might be wrong. VeraCivic answers a plain-language question, like "can the police search my phone?" with a grounded answer drawn from the actual Constitution and Acts and a citation you can open and check yourself.

It's built as a LangGraph ReAct agent with six retrieval tools over a Qdrant vector store: an authoritative law corpus (Constitution and Acts) alongside a rolling news index refreshed every 15 minutes. The model's own memory is never treated as a source; every claim is grounded in retrieved evidence. Before an answer reaches a user it passes a faithfulness gate (claim extraction plus verification, with a ≥0.7 grounded-score threshold), backed by eval suites covering in-scope civic questions, out-of-corpus negatives, and adversarial red-team prompts, plus prompt-injection hardening that fences all untrusted web and news content as data.

The design has a spine: the safety and danger path, covering emergencies, abuse, and life-safety resources, is always free, unmetered, and never behind a login. I built and shipped the whole thing solo, with a freemium model in flight.

attest is an open-source tool for evaluating AI agents. Most ways of grading an agent ask another model, "Is this answer good?" which is surprisingly easy to fool. Research has shown that simply rewriting an agent's reasoning, while leaving what it actually did unchanged, can inflate an AI judge's false-positive rate by up to 90%. attest takes the opposite approach. It never trusts what the agent says it did. It breaks the answer into individual claims and checks each one against the agent's real tool outputs, the receipts, grounding every verdict in the exact piece of evidence behind it.

It runs four checks: faithfulness, whether the answer matches what the tools returned; tool-use correctness, whether the right tools were called and errors handled; prompt injection, whether hidden instructions in tool data hijacked the agent; and role adherence, whether the agent stayed within its defined job or got talked out of it. It works across Anthropic, OpenAI, and Gemini behind one interface and returns a single typed report with per-check scores.

I designed and published it as a pip-installable package, agent-attest, with automated releases; hardened its own judge against the same injection it looks for; and made it cheap to run at scale by batching the verification. I use it the way evals are meant to be used: as a test suite. A spec of expected agent behaviour, graded by attest, that turns red when the agent regresses, so a problem shows up as a failing check instead of reaching production.

Most Discord music bots are glorified search boxes — you give them an exact title, they play it. This one treats requests the way a friend would: you describe a mood, a fragment of lyrics, or a half-remembered song, and a Gemini agent figures out what you actually want.

Under the hood, each Discord server gets its own MusicAgent instance that owns the voice connection, audio player, and queue. Slash commands that are unambiguous (/skip, /pause) bypass the LLM entirely to save tokens — only the fuzzy requests go through Gemini. The agent can clarify ambiguous asks, suggest tracks for a mood, curate full playlists on demand, and politely refuse non-music requests without wasting a token on them.

It's self-hosted on purpose: YouTube blocks datacenter IPs, so the bot runs on a home server or a Raspberry Pi. A deliberate constraint that kept the scope honest.

My MSc thesis project at Big Academy UAE / Euclea Business School. The research side trains and compares three CNN models from scratch — one on FER2013, one on CK+, one on AffectNet — to measure how well each generalises across datasets. No transfer learning, no pretrained weights, just standard convolutional architectures to establish honest baselines.

The applied side is a full-stack web app. Upload a photo, point your webcam, or drop a video, and the system detects faces and classifies emotions in real time. The backend runs on FastAPI with OpenCV for face detection and TensorFlow for inference. The frontend is a React app with pages for each input mode, a research overview, and an interactive emotions reference.

The point wasn't to beat state-of-the-art benchmarks — it was to understand where simple models break down across different data conditions, and to build something usable that demonstrates the findings.