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Sales Manager with strong skills in leading sales teams, building customer relationships, and achieving monthly and yearly sales targets through smart planning and teamwork.

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Tastemaker
Tastemaker
Gone streaking
Gone streaking
Gone streaking 5
Gone streaking 5

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Founders, be honest: are you building an idea AI gave you?

No judgment, genuinely curious. How many of us asked ChatGPT for app ideas at some point, then ended up building one of them, or half-building it? The line between "my idea" and "the model's idea" got blurry this year. Where did your current project actually come from?

Are you "Team Filesystem" or "Team Vector Search" for AI Memory?

When it comes to AI Memory, everyone's arguing "RAG vs. grep" like it's a religious war. It's not. It's just a cost curve. I've gotten this question so many times that I thought I'd just share my thoughts. So here goes:
Vector search wins when your corpus is massive, messy, and unstructured. Thousands of docs, no clean boundaries, meaning matters more than exact words.
Filesystem plus grep wins when your corpus is structured and actually yours. A folder of markdown files you can open, read, and audit line by line. No infra required.
Anthropic already ran this experiment in production. Claude Code dropped its RAG pipeline for plain agentic search (grep, glob, read) and it outperformed the vector pipeline on real work. Not close.
But the benchmark wars are missing the actual point. It was never about picking one. It's about knowing what each layer is for.
Markdown is your source of truth. Portable, human readable, greppable, not locked to one provider. Memory you can actually own and move.
Vector search is an accelerant on top of that truth. A fast index for when the haystack gets too big for exact match to keep up.
Use either one alone and it breaks down:
- Markdown alone stalls at scale and struggles with paraphrasing or fuzzy recall
- Vectors alone turn your memory into a black box you can't read, audit, or export
The next step for memory infrastructure isn't picking a side. It's the filesystem as the ledger and RAG as the index on top of it, so memory stays legible and portable, and still fast when it needs to be.
This is the exact direction we're building with AI Context Flow: markdown as the portable, ownable source of truth, with retrieval layered on top instead of replacing it.
If your memory only exists as embeddings inside someone else's vector DB, that's not memory. That's a lease.
Which team are you on?

🦊 Netfox 0.11.0 — Netfox now speaks your language! 🌍

Just shipped a big one: Netfox is now fully localized.

New languages: Italian French German Spanish (alongside English) and it follows your Mac's system language automatically, no setting to flip.

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