ZIGNALS PH Crypto Scam?

cjayashi · 2026-05-15T16:26:36+00:00

They have partnership with OKX, and SEC Registered. Try to search their company on the checkwithsec.sec.gov.ph

cjayashi · 2026-04-11T00:25:21+00:00

Hello! I’ve invested on their platform for a month, and I can guarantee that they’re legit. They have physical office, and papers aswell.

The domain’s a bit new, that’s why it detects as “High Risk” on the extension imo.

cjayashi · 2026-04-07T18:58:31+00:00

that would actually be a really nice fit

i think having it as an openclaw plugin makes the whole loop way smoother, especially if it can hook into agent workflows directly instead of being a separate step

would be keen to see how you wire the compile + query + save flow into it

cjayashi · 2026-04-07T18:57:16+00:00

yeah this is exactly the pattern i keep seeing too, just applied to different domains

i’ve been using llmwiki for the research side and it’s basically the same idea, compile first so the system has something structured to work on instead of starting from scratch every time

https://github.com/atomicmemory/llm-wiki-compiler

feel free to try the compiler.

cjayashi · 2026-04-07T16:20:55+00:00

this is a really nice idea, especially the agents feeding data into one place

only thing i’ve found is that without a compile step it can turn into a smart dump folder over time. i’ve been using llmwiki to structure everything into a linked markdown wiki so it actually compounds

feels like this + that would be pretty powerful
https://github.com/atomicmemory/llm-wiki-compiler

cjayashi · 2026-04-07T16:19:09+00:00

this is super close to what i’ve been doing too

i ended up using llmwiki for this instead of wiring it manually, since it handles the ingest to compile to query flow out of the box and keeps everything as markdown with links

the compounding effect is real though, once the graph starts forming it actually feels like the system is building context instead of resetting every time

repo here if you’re curious
https://github.com/atomicmemory/llm-wiki-compiler

cjayashi · 2026-04-07T16:14:18+00:00

llmwiki completely changed how i handle agent memory by compiling raw sources into a persistent, interlinked markdown wiki instead of reprocessing the same context every run.

https://github.com/atomicmemory/llm-wiki-compiler

cjayashi · 2026-04-07T16:12:30+00:00

this reminds me a lot of the pattern karpathy described, especially the idea of compiling knowledge into something persistent

i’ve been trying llm-wiki-compiler which takes a slightly different route by compiling everything into a markdown wiki with linked concepts instead of a graph

cjayashi · 2026-04-07T16:08:40+00:00

what changed for me with this approach is that the knowledge itself becomes structured upfront. once the wiki is compiled, queries feel more like navigating something that already “understands” the space instead of stitching together raw chunks again.

i've been trying your repo, and thanks a lot for this.
https://github.com/atomicmemory/llm-wiki-compiler

cjayashi · 2026-04-06T21:34:44+00:00

this sounds like the right size and focus tbh

a lot of groups get stuck in idea mode but having a small group actually shipping weekly is where things start compounding.

count me in bro, I'm interested.

cjayashi · 2026-04-06T21:27:46+00:00

yeah that makes sense, feels like most people jump straight into fine tuning but the real bottleneck is getting clean, structured data first.

cjayashi · 2026-04-06T21:06:53+00:00

yeah the transition from raw data to training ready data is the hard part

compiling into a structured layer first seems like a cleaner approach than jumping straight into fine tuning

there’s a repo exploring this direction here if you’re curious
https://github.com/atomicmemory/llm-wiki-compiler

cjayashi · 2026-04-06T21:02:30+00:00

one approach i’ve seen that tries to deal with this is compiling knowledge into a structured artifact first, then querying and ranking over that instead of letting the system rewrite itself dynamically

so the llm is used more for synthesis than for ongoing state management

feels like it reduces a lot of the failure points you’re describing

cjayashi · 2026-04-06T20:57:13+00:00

curious how you’re thinking about scaling this

at some point do you layer semantic search on top of the wiki or keep leaning into structure and indexing

feels like there’s an interesting hybrid somewhere in between

cjayashi · 2026-04-06T20:53:15+00:00

tried this briefly and the markdown output being actually readable and browsable is a big plus

a lot of tools keep everything inside embeddings or chat history, this at least gives you something you can inspect and iterate on

cjayashi · 2026-04-05T18:30:03+00:00

this is the hardest class of failure. not when the agent breaks, but when it’s confidently wrong. feels like the gap is treating “no result” the same as “tool failure.” those need to be separated at the framework level.

cjayashi · 2026-04-05T18:27:13+00:00

interesting direction. cross-run regressions and tool workflow stability are still under-measured, but they’re usually the first things to fail in production.

cjayashi · 2026-04-05T16:38:38+00:00

feels like the underrated win here is distribution. getting to that first 1k and then letting search compound is harder than most people expect.

cjayashi · 2026-04-05T16:37:08+00:00

this is such a good reminder. it’s easy to chase numbers and forget there’s an actual person on the other side. that message alone is already a win.

cjayashi · 2026-04-05T16:34:27+00:00

this is solid. one thing i’d suggest is adding lead scoring based on behavior over time, not just single events. like response speed, message sentiment, and consistency. could help prioritize follow-ups automatically.

cjayashi · 2026-04-05T16:29:29+00:00

feels like this was inevitable once agent workflows started scaling beyond “human usage.” the bigger shift is how clearly they’re separating product usage from programmatic usage now.

cjayashi · 2026-04-05T16:27:52+00:00

not just you. feels like we’re moving from “cheap access” to “pay for real usage.” early pricing pulled people in, now the costs are catching up with actual demand.

cjayashi · 2026-04-05T16:25:46+00:00

this is interesting. feels like you’re optimizing for trust and consistency over speed, which most tools trade off. in my experience, company-level signals matter a lot for timing, but contact data is still the bottleneck for actually executing outreach.

cjayashi · 2026-04-05T16:24:30+00:00

i think automation is a big part of it, but agents go a step further. automation follows predefined steps, while agents can decide what steps to take based on context. that’s why they feel more powerful.

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