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This is a project that I titled Low-cost AI Power theft detection that detects electricity theft, bypass of electricity, high electricity usage, with the help of TinyML integrated and trained into the esp32 and alert with the help of the buzzer and shows in a well polished web dashboard, sensor used would be communicated after the project as been finally built and ready for implementation.
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A self-initiated product campaign exploring how AI-generated concepts can be evaluated, art-directed, and refined into cohesive advertising visuals for a premium magnetic power bank.
Love the overall quality of this project. Everything feels very intentional and well executed.
My core stack is less "generate pretty pictures" and more "build the pipeline that does the thinking":
n8n — the glue. Great for orchestrating multi-step agent workflows and connecting APIs without writing a service from scratch. Weakness: gets messy fast on complex branching logic, and debugging a broken node chain eats time.
RAG (usually with a vector DB + LangChain-style retrieval) — lets me ground LLM output in actual client data instead of hallucinated generalities. Strength: dramatically improves accuracy for domain-specific asks. Weakness: chunking/retrieval quality is finicky, and a bad embedding setup silently tanks results without throwing an error.
Claude/GPT for reasoning + code — my default for anything needing judgment: analysis writeups, SQL/Python generation, proposal drafting. Strength: fast iteration. Weakness: still needs a human check on numbers — confident-sounding ≠ correct.
Power BI / Tableau — where the AI output actually becomes something a client trusts. AI can shortcut the analysis, but the dashboard is what gets signed off on.
The honest lesson: the "AI" part is maybe 30% of the workflow. The rest is validation, data plumbing, and making the output legible to a non-technical stakeholder.
Silent failures are what I watch closest. I treat an answer as untrusted unless I can show exactly which source text the model read, because fluent output can hide a bad match.
My topic:Livestream & VTuber
Hi, I saw online that IRL LiveStreaming is one of the hottest niches for some time now, but it is also the most dangerous one. I saw multiple videos of streamers being intimidated, provoked and even harassed.
This is not about streaming, its about humans feeling safe.
Today, in 8 full focused hours I put the pillars of the project PulseOS.
It gives updates to the creator about:
-Idle: basic state
-Notifications : ex: one new paid subscriber
-Ai-Summaries: ex: summary of the last 5 minutes of the live-chat energy
-Critical-Alert: ex: someone is following a creator, the chat is typing more than the usual flow of messages->activates triggering automation->creator sees what the chat is talking about
I projected this idea with 7 pillars, so here are the next 3, which I sacrificed for this demo, to be able to do the 4 pillars as intended.
The other 3 pillars:
-Stream health: ex: camera battery %, temperature of CPU
-Focus mode: ex: when streamer is serious, notifications are subtle almost non-existent for that time
-Achievement: ex: 1000 followers in the past month, 12 hours live in the past 3 days
I got around the paywall and prototyped a flux on notification with Make.com on HTML with POST Requests with the help of Ably.
Rive App Link:https://editor.rive.app/file/pulseos/2491853?linkId=2t4qGJlgK0GkdLA8qFPpDw
X link:https://x.com/ydgsgiwhs/status/2085972264185409606?s=20
future note, you can "share" a file on the rive freeplan by making a copy and putting it into the 'Shared Project',
then in the file using the invite button in the top right to get a link. I say a copy because once you put a file in a shared project you can't move it back to a...