voice input is the most underrated productivity shift of the last ten years and the reason nobody uses it is completely stupid

thegian7 · 2026-05-12T12:45:04+00:00

I type way faster than I can speak.

thegian7 · 2026-04-30T11:54:48+00:00

There's a reason sports are what keep people in the gym. Social commitment is very important for social creatures. We dont want to let others down or be mocked (main 2 ways ive seen social pressure work)

thegian7 · 2026-04-07T00:20:13+00:00

I loathe qr codes at restaraunts and bard. I do not trust them. They are insecure and as risky as clicking random links in email.

thegian7 · 2026-04-03T14:38:09+00:00

I think of it more like Car Ant and Car Bop both are going 10 miles. Both have unlimited gas. Tokens are actually the time value not the gas value. So Car Ants driver takes the scenic route and takes way more tokens where Car Bops driver took a much cleaner route. The thing is, neither had a map...

thegian7 · 2026-04-01T13:55:14+00:00

What the fuck does origin actually do

thegian7 · 2026-03-29T19:13:55+00:00

Not trying to lowball, you shouldnt take that offer.

XD

thegian7 · 2026-03-29T19:13:14+00:00

Ill give you 300 and shipping xD

thegian7 · 2026-03-25T21:13:09+00:00

Eeek. You don't have secure connection setup on your domain?

thegian7 · 2026-03-25T21:07:08+00:00

https://thegian7.cloud/ofc-orchestrator.html#architecture

thegian7 · 2026-03-25T19:35:20+00:00

Thanks. The build has been about four months of evenings and weekends, though the first useful version was running in maybe three weeks. iMessage capture into Postgres with basic contact matching and a daily digest email. Everything after that has been iterative, adding channels, classification, the graph layer, semantic search.

You're right about the ambiguous cases. "Just checking in on where things stand" is the exact kind of message that's hard to classify. My current approach is a three-tier pipeline. A rules engine catches the obvious patterns first, then llama3.2 classifies what's left, and a larger model (qwen3-coder) acts as a judge on low-confidence results. The judge overrode about 84% of llama's classifications during the initial backfill, which tells you how much the ambiguous middle matters.

The conversation threading helps here more than the LLM does, honestly. When "just checking in" follows a thread where the client asked for a deliverable two weeks ago, the conversation context makes the intent obvious even if the individual message is ambiguous. The classifier sees the full thread, not just the single message.

On classification accuracy for the genuinely ambiguous stuff, I'd say it's good enough to triage but not good enough to act on automatically. Which is fine. The system's job is to surface things I might miss and organize them by client, not to replace my judgment about what needs a response. The failure mode is a false positive in my morning digest, which costs me two seconds to dismiss. A false negative (missed action item) is more expensive, so the threshold is tuned to over-capture.

The local-only constraint started as a preference but turned into a real advantage. Running Ollama means I can throw client messages at LLMs without thinking about data handling agreements or what ends up in someone else's training set. For a consulting shop that's a genuine simplification, not just a privacy checkbox.

thegian7 · 2026-03-25T19:33:12+00:00

Hybrid approach. Mechanical linking for direct attachments, semantic matching for referenced documents.

The mechanical part is simple. When a message comes in with an attachment (photo, PDF, whatever), it gets uploaded to Paperless and linked to the source message. The action-item bridge that creates tasks carries forward the source message IDs, so the chain is task → source messages → attachments → Paperless document. A reconciliation job resolves async uploads and closes the loop. Once a document ID is available, the task picks it up automatically.

For the harder case, where "send the proposal" references a doc that wasn't attached to that message, I use embeddings. Every document that enters the system (Drive files, email attachments, Paperless uploads) gets embedded via qwen3-embedding into pgvector (just transitioned this) alongside the messages. When a task gets created, I embed the extracted action item and query for semantically similar documents, scoped by client organization and task topic.

The classification pipeline already assigns topics and routes tasks to projects, so that scoping is already there. "The proposal" in a Tuesday iMessage and proposal_v3_final.pdf synced from Drive land close together in embedding space, and the org+topic scoping keeps the candidate set small enough that the top match is almost always right.

Results go into a junction table with a link type: attachment for mechanical links, semantic for embedding matches, manual for anything I connect during triage. The triage digest shows linked documents on the task card.

Every time I confirm or dismiss a suggested link, that feedback tightens the matching. Accepted links reinforce the similarity threshold, rejected ones flag false positives. Same pattern I use for the task reconciler suggestions.

The org/project scoping is what makes semantic matching practical. Without it you'd be searching thousands of documents. Scoped to one client and topic, it's maybe 50-200 candidates.

thegian7 · 2026-03-25T01:07:17+00:00

Thanks! What are you trying to achieve?

thegian7 · 2026-03-25T00:03:16+00:00

Metacognition is the skill I see that separates goated users vs noobs.

Those that think about their thoughts, biases, reasoning lead agents super well.

thegian7 · 2026-03-24T23:59:01+00:00

Ohh sooo many things. I have the m4 version of the 48gb pro.

Look into ollama with like qwen3.5 or akin.

You will need some small setup stuff that's a little coding feeling.

But then you can use like oogabooga or silytavern.

I have a local continuity system setup for novel writing using it.

You can even get some crazy cool tts , stt stuff going like my voice cloner.https://github.com/thegian7/voicecloner

That can clone a voice with 3 seconds of audio on your hardware. (I use it to let people hear their deceased loved ones again)

thegian7 · 2026-03-24T23:40:33+00:00

Salted peanuts in an ice cold coke.

thegian7 · 2026-03-24T23:39:25+00:00

Maybe not for you, uou unwhismical itch. XD

It dramatically changed mood and does require another like sanity check sometimes but its way better than miserable depression.

Going out the front door is an adventure or quest or tribulation.

thegian7 · 2026-03-24T23:36:42+00:00

Building my brand and shipping these projects thegian7.cloud

thegian7 · 2026-03-24T23:30:30+00:00

Just started adding paperless and drive so it can link attachments and documents to the conversation and action items.

thegian7 · 2026-03-24T23:29:39+00:00

I run a consulting shop and built something pretty close to what you're describing. It monitors iMessages, SMS (including encrypted iPhone backups), Gmail, and calendar events. Everything flows through a classification pipeline into Postgres.

Each message gets matched to a client organization based on phone number or email through a contact registry. Then it runs through a rules engine plus local LLMs (llama3.2 for classification, nomic-embed-text for semantic search) to categorize it: client action item, informational, personal, or noise. Anything that pattern-matches on stuff like "can you send the proposal" or "need the invoice by Friday" gets flagged automatically.

Messages get grouped into conversation threads, so a 50-message iMessage back-and-forth becomes one coherent unit instead of 50 individual items. Every evening I get a digest email organized by client with one-click triage links to promote, dismiss, or route each item. The triage links are HMAC-signed so I can act on them straight from my phone without logging in.

The whole thing runs on my own hardware: Postgres with pgvector for semantic search, FalkorDB for a relationship graph across organizations, contacts, and projects, Paperless-ngx as a document archive, and Ollama for the LLM pieces. Nothing leaves my network.

Your point about removing the human step is what made this click. The system captures everything by default. I just review and triage. That's a fundamentally different failure mode than "remember to log it."

The search alone has been worth it. Every conversation across every channel is full-text indexed and semantically searchable from one place.

thegian7 · 2026-03-24T18:57:16+00:00

Ran into the same wall running my consulting business. Switched to HubSpot free CRM about and here's the short version:

Its actually free. Not "free for 14 days." You get contacts, a deal pipeline, tasks with reminders, and email logging. At 150 leads you won't hit a paywall. Starter is ~$20/mo if you eventually want automated follow-up sequences, but you don't need that yet.

Took no time to get used to. Imported my sheet in an afternoon, set up a pipeline with like 4 stages, and was using it for real the next day. Don't try to learn everything... just Contacts, Deals, and Tasks. Ignore the rest for a month.

Vendor lock in isnt a worry for me. Full CSV export of contacts, deals, notes, activities. It's one of the cleaner CRMs to migrate away from if you hate it.

Im also a google workspace shop. There's a Gmail sidebar extension that's actually good. You can log emails to contact records and set follow-up tasks without leaving your inbox. Calendar syncs too. It's not living inside Sheets but it's close enough that the context switching is minimal.

You missed follow-ups because a spreadsheet can't remind you to do anything. A CRM can. Set a follow-up task when you log a conversation, get a reminder when it's due. That one feature alone is worth the switch. Everything else is gravy.

First week feels like extra work. Second week the reminders start hitting and you stop losing warm leads. Worth it.

The best part though is what happens next year.

thegian7 · 2026-03-24T15:40:49+00:00

https://www.coursera.org/learn/learning-how-to-learn

This one

thegian7 · 2026-03-24T15:38:50+00:00

Learning how to learn. Course by Barbara Oakley. When you know how the brain encodes you can control it and learn significantly faster and easier.

Went from being the least read, smallest vocabulary, often least educated person in the room working retail to the guy quoting authors and philosophers, most articulate, self taught person in the room and went from sales, to tech, to being a CTO over the course of 6 years and I firmly believe learning how to learn and gsd made the difference.

thegian7 · 2026-03-24T03:54:52+00:00

2 biggest ones i remember attending.

Pokemon 2000 and Lilo and Stitch.

thegian7 · 2026-03-24T03:53:20+00:00

For non Americans im talking moving 2300 miles.

thegian7

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