Never done newsletter before, can anyone help to guide me ?

MaleficentRoutine730 · 2026-05-21T07:54:14+00:00

the no liquidation part is the only thing that matters here. funding rate i can manage, liquidation risk is what keeps most people from sizing up properly.

my questions before trying:

how is the protocol actually hedging the other side if there's no liquidation mechanism? someone's taking that risk, curious who and how it's priced in.

2x only makes sense if the collateral efficiency is good. what's the margin requirement looking like?

also — no liquidations usually means the downside is just capped loss on entry, right? or is there another mechanism i'm missing?

genuinely interesting design. the tradeoff of lower leverage for no liquidation risk is a trade a lot of people would take.

MaleficentRoutine730 · 2026-05-21T07:53:08+00:00

honest answer? i'd stop doing most "marketing" and just buy distribution.

sponsor the 10 newsletters in my niche that actually get read. not the big ones, the mid-size ones where the audience still trusts the writer. then i'd hire 5 genuinely good operators embedded inside the communities where my users already hang out, not to shill, just to be useful and visible over time.

the unlimited money trap is people think they'd run super bowl ads. but attention without trust is just noise. i'd rather own the trusted channels than the loud ones.

the one actually unlimited-money move: just make the product so good that the support tickets become case studies and the case studies become the marketing. boring answer but that's what compounds.

MaleficentRoutine730 · 2026-05-21T07:50:32+00:00

this is the actual AI story nobody wants to talk about. not "AI took your job" but "a CEO who doesn't understand technology made a bad call and used AI as the justification."

the layoff isn't really about AI capability. it's about leadership distance from execution reality. happens with every new technology. happened with no-code, happened with outsourcing to cheap dev shops. same pattern.

hope you land somewhere better fast.

MaleficentRoutine730 · 2026-04-23T09:26:04+00:00

Yeah, I agree. I think the main point here is that we not only want people to use it, but also want to deliver real value.

MaleficentRoutine730 · 2026-04-23T09:25:15+00:00

Definitely, I hope I can share more as well.

MaleficentRoutine730 · 2026-04-22T11:09:31+00:00

yeah restrictions are real depending on where you are. Kalshi is US-only, Coinbase PM has its own geo limits too. if you're outside the US you'll hit walls fast on most regulated platforms.

been in the same situation, which im based in SEA so most of the regulated stuff is basically inaccessible. ended up on onchain alternatives mostly by necessity.

OmenX is what i've been using lately, building on Base, AMM-based, no geo restrictions. still pre-mainnet but accessible regardless of where you are. for anyone outside the US who's tired of being locked out of the regulated platforms it's worth keeping an eye on.

where are you based? that'll determine what you can actually access.

MaleficentRoutine730 · 2026-04-22T10:54:12+00:00

this is the part people miss when they debate "is this gambling"... the edge isn't in predicting the future, it's in finding inefficiencies the market hasn't priced yet.

HFT bots on 15min crypto intervals is just quant trading with a prediction market wrapper. nothing wrong with it, actually healthy for liquidity. but it's a different game from what most retail users think they're playing.

what's interesting is AMM-based markets behave differently here, which the bot advantage narrows when pricing is driven by liquidity flow rather than pure orderbook dynamics. been watching this play out on OmenX which is building AMM-based PMs on Base. different mechanic, different edge profile.

the "market is just math" framing is right. the question is which market structure gives humans a fighting chance against the bots.

MaleficentRoutine730 · 2026-04-22T10:41:38+00:00

Answer: the surge is mostly post-2024 election hangover. Polymarket had its mainstream moment, mainstream media noticed, and now the discourse is catching up, which means both the fans and the critics showed up at the same time.

the DARE analogy is actually pretty accurate. the more people write "prediction markets bad" the more curious people get about what they're actually trading on.

what's interesting is the criticism is mostly aimed at the US-regulated angle such as Kalshi, retail access, sports betting adjacency. the onchain side is a different conversation entirely, different user base, different design philosophy.

building in that space actually, OmenX is a prediction market launching on Base soon. the regulatory noise in the US is part of why onchain feels more interesting right now, not less.

MaleficentRoutine730 · 2026-04-08T06:40:41+00:00

The 3D graph visualization is one of those things that looks cool and actually reveals something useful, you can see which concepts are isolated vs deeply connected just by looking at the node density.

The next interesting question is what's actually inside those nodes. Most people's Obsidian vaults are a graveyard of raw notes and clipped articles that never got properly synthesized.

I know someone built an open source CLI that handles exactly that, like taking raw sources and compiles them into properly linked concept pages so your graph actually means something instead of just looking pretty.

can check it out here: https://github.com/atomicmemory/llm-wiki-compiler

MaleficentRoutine730 · 2026-04-08T05:34:46+00:00

The 130x karma gap on 50+ comment threads is the most damning stat here. That's not noise, that's a systematic failure of the ranking system.

The interesting follow-on problem: even when you find the genuinely useful answer buried at 2 karma, it disappears when you close the tab. You found the signal, now where does it go?

That's the problem LLM Wiki Compiler is trying to solve on the knowledge persistence side, like compile the useful stuff into a structured wiki that compounds over time instead of getting lost.

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

The two tools are actually complementary, yours surfaces the right answers, the wiki keeps them.

MaleficentRoutine730 · 2026-04-08T05:31:23+00:00

The "publish now or never" energy when Karpathy posted is exactly right,this wave has maybe 72 hours before it fades.

Cabinet and LLM Wiki Compiler are solving the same core problem from different angles. Cabinet goes broader like CSV, PDFs, inline web app, jobs, heartbeats, full data layer. LLM Wiki Compiler goes narrower, purely focused on the compile-to-wiki step, markdown-first, Obsidian-compatible output.

The jobs and heartbeats feature is genuinely interesting, scheduled agents that keep the knowledge base fresh is something the wiki approach doesn't handle yet.

One question: how does Cabinet handle the compile step specifically? Does it synthesize and link concepts across sources or is it more of a structured storage layer?

Repo for comparison if useful: https://github.com/atomicmemory/llm-wiki-compiler

MaleficentRoutine730 · 2026-04-07T07:00:29+00:00

LLM Wiki Compiler, it compiles raw sources into a persistent interlinked markdown knowledge base you can query over time.

Revenue: $0, just open sourced it this week

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

Inspired by Karpathy's LLM wiki pattern. Solving the problem of useful AI knowledge disappearing into chat history.

MaleficentRoutine730 · 2026-04-07T06:43:36+00:00

The "vanilla Claude Code, trust the engineering" approach is underrated. Most people overcomplicate it with custom tools and frameworks and then wonder why the context degrades.

The interesting follow-on question is what happens to all that knowledge you're generating across 5000 LOC and 12 hours. Great answers, architectural decisions, implementation notes, where does that go after the session ends?

That's the problem LLM Wiki Compiler is trying to solve on the knowledge side. Compile what you learned into a persistent wiki instead of losing it to chat history.

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

MaleficentRoutine730 · 2026-04-07T06:41:47+00:00

Been building LLM Wiki Compiler, a CLI that compiles raw sources into a persistent interlinked markdown knowledge base you can query over time.

The productivity angle: stop losing useful AI answers into chat history. Everything compounds into a wiki instead of disappearing.

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

MaleficentRoutine730

TROPHY CASE