متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Somo Crypto & Ai Reads
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Crypto, Capital Markets, AI In Service of Freedom. TG: @somonaut X: https://x.com/somonaut
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منشورات القناة
We’re building Dekant, a distribution market where you can trade the full shape of your belief, not just one yes/no outcome.
Right now, we’re looking for prediction market traders, options traders, quant traders, and sharp discretionary traders to try Dekant early.
If that’s you, DM me @somonaut.
As an early user, this is what you’ll get access to:
https://x.com/embrron/status/2069529890660233496
| 2 | Freddy is my favorite vibe coder; he builds lots of cool stuff. Check his TG channel: @testedinprod | 1 013 |
| 3 | Kalshi Calendar
Upcoming Kalshi markets in a normal calendar, with filters for economics, earnings, sports, politics, crypto, and more.
→ https://kalshicalendar.xyz
https://x.com/freddy_0x/status/2069477356549685419 | 198 |
| 4 | ZUCKERBERG DIRECTED META TO BUILD PREDICTION MARKETS APP: NYT
Source | 98 |
| 5 | Liquid funds Average entries
https://x.com/tumilet/status/2069314653922931088 | 273 |
| 6 | Birdclaw turns your Twitter archive into a local SQLite database.
Tweets, DMs, likes, bookmarks, mentions, blocks, mutes, and graph data become searchable with FTS5. It also has cached live sync, AI ranked inbox triage, daily digests, and Git friendly JSONL backups.
Your X history becomes private infrastructure instead of rented memory.
→ https://birdclaw.sh/ | 327 |
| 7 | v | 1 |
| 8 | Four essays on thinking, conversations, self awareness, and the waiting period before things click.
Third order thinking: how to build a life that can't trap you
https://thejangg.substack.com/p/third-order-thinking-how-to-build
why some conversations rearrange your brain
https://velvetnoise.substack.com/p/why-some-conversations-rearrange
The Paradox Of Self Awareness: You Think Too Much, And That's Your Problem
https://ixcarus.substack.com/p/the-paradox-of-self-awareness-you
surviving the liminal space before your dreams come to life
https://erifili.substack.com/p/surviving-the-liminal-space-before | 276 |
| 9 | INDEX VENTURES, UNION SQUARE VENTURES BACK TRADING APP FOMO AT $550 MILLION VALUATION: FORTUNE
https://t.me/PhoenixNewsImportant/929 | 246 |
| 10 |  Mo on prediction market LPs:
LPs in PMs are not really market makers.
They look like spread traders on screen, but in P&L they behave more like bookmakers or underwriters, taking the other side of biased flow and carrying the residual tail to settlement.
The clean line here is the “optimism tax”:
casual money buys Yes 61% of the time, while Yes only settles 33% of the time. The book wins the gap.
Also a good framing shift:
the real scarce input is not quoted spread, it’s capital willing to absorb correlated tail risk and survive it.
https://x.com/embrron/status/2068441787497353336?s=20 | 293 |
| 11 | Mo on prediction market LPs:
LPs in PMs are not really market makers.
They look like spread traders on screen, but in P&L they behave more like bookmakers or underwriters, taking the other side of biased flow and carrying the residual tail to settlement.
The clean line here is the “optimism tax”:
casual money buys Yes 61% of the time, while Yes only settles 33% of the time. The book wins the gap.
Also a good framing shift:
the real scarce input is not quoted spread, it’s capital willing to absorb correlated tail risk and survive it.
https://x.com/embrron/status/2068441787497353336?s=20 | 1 |
| 12 |  The X Algorithm, Decoded
10 page field manual on how distribution on X actually works, reverse engineered from the open source code.
Covers:
• what drives reach
• what most people get wrong
• which signals matter most
• the 7 moves that matter if you want more distribution
Comment "access" and I’ll send it.
https://x.com/somonaut/status/2068315475894014426 | 276 |
| 13 | I've been working on the Twitter algorithm for a while. The algorithm has changed a bit recently, and to help you easily understand how to grow on it, I've put together a guide. You can check it out. @somoreads | 79 |
| 14 | https://x.com/hl_eco/status/2067963684144464011?s=20 | 254 |
| 15 | https://x.com/aporia9n/status/2067916098113085529?s=46&t=nluSw7cwYGx8zFxfoYi5Iw | 245 |
| 16 | Tactical vs Strategic programming
In my latest post, I was basically circling around this idea:
"AI doesn’t remove engineering
it moves the bottleneck"
Then I listened to this podcast, & Matt put it in a much cleaner, almost textbook way:
tactical programming vs strategic programming
This may feel repetitive, but is not
sometimes the right definition makes it click for u
tactical programming is the ground-level work:
writing code
fixing syntax
debugging small issues
moving files around
making commits
implementing scoped tasks
LLMs are already gud at this now
strategic programming is the higher-level work:
how should the system be designed?
where should the boundaries be?
what should be easy to change later?
how should this be tested?
what should the agent do, and what should the human decide?
that’s where software engineering is moving
& this explains the senior vs junior gap pretty well
juniors get a boost from AI because they can produce code faster
seniors become 100x, because they can design the system
they can c bad abstractions
they understand tradeoffs
they know where bugs usually hide
they know what needs tests
they've seen millions of lines of code before
most of us are not senior devs
so the real question is:
what should we learn?
not more syntax
not React tutorials
def not switching tools every week
the highest leverage stuff is around the code:
Git
debugging
reading errors
testing
APIs
databases
basic architecture
deployment
security basics
how software actually ships
Matt has a “teach” skill in this repo:
https://github.com/mattpocock/skills
install it with ur agent & give it a mission:
"i’m a vibe coder
i know basic terminal commands
i can read some code
i want to build a real full-stack app
what should i learn next?"
it creates a local learning path around that mission:
mission file
learning record
HTML lessons
quizzes
reference sheets
next steps
this is probably the right learning model for the post-LLM era:
build aggressively with AI
but use every project to learn the fundamentals underneath
u want to mainly learn from mistakes in the post llm world:
bugs teaches debugging
bad agent PR teaches code review
messy file teaches structure
failed deploy teaches shipping
btw, me & a fren r putting together a doc around exactly this:
what parts of the software engineering syllabus actually matter now?
the input will be high-quality software engineering books & university syllabuses, focusing on strategic programming angle
I believe universities/academia r well behind the schedule on this & will have to transform their courses accordingly in CS departments, but until then v have to come up with our own learning roadmap
on an irrelevant note, I have to mention GLM 5.2 here, it's insanely guuud, experts r comparing the experience to CC Opus 4.8/ GPT 5.5:
https://x.com/PatrickToulme/status/2068134212587184442
https://x.com/i/status/2067757468189679764
if u want to try it, OpenCode Go is probably the easiest option & dirt cheap too (like 5 bucks) | 209 |
| 17 | https://x.com/bullpenfi/status/2067652267227193766?s=52 | 263 |
| 18 | https://x.com/lmrankhan/status/2067718954705915993?s=52 | 311 |
| 19 | https://theascent2045.substack.com/p/the-ascent | 346 |
| 20 | this is very scary | 363 |
