Quaramly

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Quaramly

Quaramly

@quaramly

Web3 Educator | Sharing alpha & guides

Katılım Ekim 2010
67 Takip Edilen161 Takipçiler
Quaramly
Quaramly@quaramly·
ONE AI WORKFLOW CAN NOW REPLACE HOURS OF SOCIAL MEDIA RESEARCH EVERY SINGLE WEEK Using the new Record a skill feature, iPhone Mirroring and Computer Use, she recorded the workflow once, then let Claude reopen the app, apply mobile-only search filters, analyze top-performing videos and compile a complete research report without touching her phone again TikTok's desktop version can't sort videos by likes, but the mirrored iPhone gives Claude access to the same filters a human would use. Within minutes, it identifies the strongest hooks, extracts view counts like 565.7K, 104.5K, 49.7K and turns them into actionable content ideas instead of endless scrolling For agencies, creators and media teams, research like this can easily consume 10-20 hours every month. Recording the workflow once means those hours can be spent creating content instead of hunting for it over and over again
Quaramly@quaramly

ONE FOUNDER BUILT AN AI TEAM THAT REPLACED HOURS OF DAILY WORK When Claude introduced the /goal command, she immediately realized it wasn't just another feature. Instead of asking AI to complete one task at a time, she built a system where specialized agents could handle customer support, investigate bugs, write code and manage different parts of his business while he focused on making the final decisions It took only a few days to turn the idea into a working workflow. Today, a single request can trigger an entire chain of actions, from answering customer emails to creating GitHub pull requests, without stopping for approval after every step The founders who benefit the most from AI won't be the ones writing better prompts. They'll be the ones building systems that keep working long after they stop typing

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Wizzy
Wizzy@CodewizzyX·
EVERYONE IS SHARING THIS CHINESE AI BLACKBOARD CLIP AS PROOF CHINA IS BEATING AMERICA AT SCHOOL, THE REAL STORY IS MORE UNCOMFORTABLE this is from the World AI Conference in Shanghai last week the board everyone is filming is being written by an AI model working through graduate level math in real time it reads the problem, lays out every step and solves it faster than the room can follow the uncomfortable part is what the board is really showing, a Chinese lab flexing how far their reasoning models have come people think they are watching star students when they are watching a machine reason through proofs on its own the thing is you already hold the same power in your pocket drop a hard problem into Claude Opus 4.8 and watch it lay out the whole solution one step at a time the gap this clip is really measuring sits between the people who run these models every day and the people still just filming them
Sancho@Sancho_Wizard

x.com/i/article/2076…

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kvinsi
kvinsi@kv1nsiii·
OPUS 5 REALLY DOES SEVERAL TIMES MORE THAN ITS PREDECESSOR. AND YET MANY PEOPLE STILL TREAT IT LIKE IT'S 4.8 Opus 5 runs on a different contract. Set a goal, put up a fence and set a bar. It finds the route, checks itself and continues to work even when you close the laptop Do it right and you get roughly 20 times the problem-solving capability of its predecessor for the same $5 price. Do it wrong and you pay the old-model tax on a new-model bill Five house rules replace a page of instructions: > Never hard-code a special case describe the behaviour once > No new dependency without asking > Anything irreversible (deployments, deletions, payments) stop and show me first > Every done comes with evidence that I can check in under a minute > When my instructions conflict with these rules, the rules win. Delete your old verification lines. Opus 5 already checks its own work. Any leftover double-check before answering prompts now cause over-verification and you pay for both passes Split the loop into a writer and a verifier. The verifier receives fresh context, assumes the work is flawed and continues to check until no real issues remain Allocate effort like payroll: low for formatting, medium for daily coding, high for real features and X-high for deep debugging and architecture Run it overnight. Build, verify against the bar, close the biggest gap and repeat. It never declares itself finished only the verifier or you can do that It's the same model that everyone has. Different contract with it
Miraqle@0xMiraqle

Total breakdown on how to take your OPUS 5 to a GOD-MODE level that works as an army of teammates x.com/i/article/2080…

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DegenCalls
DegenCalls@Degen_calls_sol·
CLAUDE OPUS 5 ONE-SHOT A GAME IN FOUR HOURS FROM A SINGLE PROMPT. Everything visible in the demo was created with custom code. No external assets. That means the game was not assembled from an existing art pack or a library of prebuilt pieces. Claude generated the result from scratch in one pass. Four hours ago, it was only an idea described in a prompt. Now it is a playable game. AI games are going to be amazing.
DegenCalls@Degen_calls_sol

x.com/i/article/2070…

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ISOfunds
ISOfunds@isofunds·
THIS GUY BUILT A MEV SANDWICH BOT AND LET IT RUN FOR 24 HOURS the whole thing was written by AI. smart contract deployed on ethereum, scans the mempool for pending swaps, bids higher gas to front-run them, sells immediately after. classic sandwich attack the setup: - deploy contract via remix, ~$1 gas - minimum 1 ETH liquidity, sweet spot 2-10 - returns taper off after ~50 ETH - 3-button control panel: start, verify, withdraw he ran it for 24 hours straight. terminal logs show every routing action and trade the contract executed. no black box — you can review exactly what happened "educational purposes only, not financial advice" — which is what everyone says right before showing passive income numbers honest part: sandwich bots work. they also extract value from other traders. this isn't a bug in the bot, it's the feature. MEV is a zero-sum game and you're on the extracting side
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Kitsune Tails
Kitsune Tails@kitsune_xbt·
THIS GUY BUILT AN AI AGENT THAT NEVER FORGETS A SINGLE THING And it changed the whole AI ecosystem he wired Claude Code into Obsidian and turned it into a memory vault that runs itself every note, every idea, every past conversation gets stored as plain markdown files the agent can read Claude Code writes new memories, updates old ones and cleans up the vault on its own you ask it something from 3 months ago and it pulls the answer instantly like it was yesterday the whole thing self manages so you never sort a folder or tag a note by hand it lives on your laptop, your data stays yours and the agent gets smarter every time you use it most people still lose everything they told their AI the moment they close the tab whoever builds their own memory layer now is going to look years ahead of everyone starting from scratch will drop a full step by step guide here on Monday so don't miss out
Yarchi@undefinedKi

x.com/i/article/2067…

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yurshev
yurshev@yurshevv·
40% of the AI agents being built right now will be dead within two years. Good. That's exactly what you want to hear. Picture a twenty-person engineering team with a seven-figure budget building an agent meant to do everything at once. It ships slow, wrong half the time, nobody trusts it, and it gets shut down within six months. Now multiply that by four out of every ten agent projects on the planet right now. While the giants are figuring that out, one person can ship a narrow agent for one client in a weekend. No committee. No budget approval. Just something that actually works. By 2028, a third of enterprise software interactions are projected to run agent to agent, no human in the loop. But before any of that happens, someone has to claim the boring, narrow niches first, because the giants won't bother touching them. That someone isn't going to be a corporation. It's going to be whoever moves first.
yurshev@yurshevv

x.com/i/article/2080…

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Sethian
Sethian@theSethian·
The first robots people fall in love with may already be under construction in China. AheadForm showed Origin F1 at WAIC 2026. The company says the robotic face can recognize people, hold eye contact, listen, speak in real time, and respond with small expressions. Now put a face like this on a full humanoid body and connect it to an AI with long-term memory. It could greet you by name, adapt to the way you speak, continue yesterday's conversation, and do a few things that will never appear in the launch video. Once a machine remembers your voice and looks you in the eye, turning it off may feel different.
Sethian@theSethian

x.com/i/article/2079…

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godgiven
godgiven@bygodgiven·
OpenAI just dropped a free workshop on making agents work for months, not minutes. The stack their own DevEx engineer runs: memory vault: git repo of markdown files (TODO, people, projects, notes) AGENTS.md that tells the agent to update the vault as it learns 9 pinned threads, one per project, months of accumulated context heartbeat automations: agent checks Slack and Gmail every 30 min, drafts replies, sends nothing goals with real oracles: "port this library to Rust, all original tests must pass" that's the stack. Save this, then set up your vault tonight. Works with Codex, Claude Code, whatever you run.
godgiven@bygodgiven

Yesterday i wrote: Opus 5 won't improve your results. Your workflow will. So here's step one. Ponytail, the plugin that stops Claude from overengineering. 88k GitHub stars, most of them in the last two weeks. Claude's default habit: you ask for a button, you get a framework. Extra files, extra libraries, extra abstractions. Every extra line is a future bug and a bigger token bill. Brutal if you're a non-coder shipping products through Claude. Ponytail injects one rule: lazy on solutions, not on thinking. It still digs into the task first, then ships the minimum that works. Security checks and error handling stay untouched. The underrated part is /ponytail-audit. Point it at your existing project and it lists everything Claude overbuilt: duplicated logic, dead libraries, bloated files. Then you ask Claude to rebuild those spots minimal. Author claims 54% less code and 20% cheaper across 12 real tasks. His own numbers, so verify on your repo, not his. Install: /plugin marketplace add DietrichGebert/ponytail then /plugin install ponytail@ponytail. Free, MIT, works in Claude Code, Cursor, Codex, Gemini. What's the most overengineered thing Claude ever built for you? Mine turned a config parser into three classes.

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Poly
Poly@poly0015iew·
Gm and Happy Sunday fam <3
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Akito
Akito@itsak1to·
Leonardo DiCaprio was offered $2.5 million for Titanic. he asked for something else instead - and it turned into $40 million. instead of a bigger check, he took 1.8% of the film's gross. Titanic became the highest-grossing movie in history to that point, and his tiny percentage quietly paid him roughly $40 million - sixteen times his salary. he'd learned the lesson most people never do: the salary is the floor, the ownership is the ceiling. then he ran the same play off-screen. he became an early investor in Beyond Meat before plant-based meat was a thing - it IPO'd at a $4 billion valuation. he backed Mobileye, Rubicon, Diamond Foundry, over 15 startups in all. not lending his name for a fee - taking a stake. which is the strange part. the role that made him a legend was The Wolf of Wall Street - a con man who got rich selling people worthless stock and spending every dollar. DiCaprio built his real fortune doing the exact opposite: owning real things and holding them. it didn't always work. Beyond Meat later collapsed below $1 billion. Casper flopped after its IPO. real equity carries real losses. a fee pays once. a stake pays as long as you hold it. DiCaprio chose the version of wealth with no ceiling - and accepted the risk that comes with it. the man who played Wall Street's most famous fraud got rich doing the one thing that character never could: owning something real ↓
Akito@itsak1to

the man who runs a $1.5 trillion company once had to personally explain why his database kept crashing. he was 19, and the site was called TheFacebook. in 2004 he ran it off rented machines from his Harvard dorm, describing the whole thing like a hobby. "when we first launched we were hoping for maybe 400 or 500 people. now we're at 100,000. who knows where we're going next... maybe we can make something cool." no business plan. no exit strategy. just a coder solving one problem at a time - how to scale to the next university, how to stop the servers from falling over, how to keep people coming back. that "something cool" now serves over 3 billion people. but here's what almost nobody in his position does. he never sold it. Microsoft and AOL reportedly offered him millions for a program he built in high school - he said no. Yahoo offered $1 billion for Facebook when he was 22, with his entire board telling him to take it - he said no. most of his senior team quit within a year. "I don't really like putting a price-tag on the stuff I do. that's just not the point." the people who build the biggest things aren't optimizing for the exit. Zuckerberg wasn't trying to get bought. he was trying to build something people couldn't stop using - and refused every offer to hand it to someone else. every buyout is a bet you're selling too cheap. the price is what someone smarter than you thinks it's worth today. it's the floor, never the ceiling. the billion he turned down at 22 is now a rounding error on what that "no" was actually worth ↓

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Happy
Happy@ArchitectHappy_·
10 graph engineering repos every AI engineer should know Microsoft GraphRAG turns unstructured documents into entities relationships communities and structured summaries use it when vector search finds relevant chunks but misses how the facts connect → github.com/microsoft/grap… KG-Gen converts text into a knowledge graph and resolves duplicate entities during construction use it when “Microsoft” “MSFT” and “Microsoft Corp” keep becoming separate nodes → github.com/stair-lab/kg-g… Nano GraphRAG a small readable implementation of the core GraphRAG pipeline use it to understand the architecture before working with larger frameworks LightRAG was originally built on top of it → github.com/gusye1234/nano… Neo4j GraphRAG build knowledge graphs and retrieve information directly from a graph database use it when your application needs graph traversal vector retrieval and production database infrastructure → github.com/neo4j/neo4j-gr… FalkorDB a low latency graph database with graph queries semantic search and hybrid retrieval use it when the answer requires both vector similarity and relationship traversal → github.com/FalkorDB/Falko… LightRAG extracts entities relationships and short descriptions then combines them during retrieval use it when full GraphRAG feels too expensive or complex for your application → github.com/HKUDS/LightRAG Graphiti stores facts in a bi-temporal knowledge graph it tracks when an event happened and when the system learned about it use it for agent memory where facts change over time → github.com/getzep/graphiti KAG combines knowledge graphs logical reasoning and multi-hop factual retrieval use it when the answer depends on several connected facts rather than one retrieved passage → github.com/OpenSPG/KAG LlamaIndex provides property graph indexes for building and querying knowledge graphs inside LLM applications use it when you want graph retrieval integrated into a broader RAG pipeline → github.com/run-llama/llam… LangGraph orchestrates long-running stateful agents as graphs of nodes edges and loops use it to define how agents remember route retry and continue execution → github.com/langchain-ai/l… the first five help you build and store the graph the next four help you query and remember through it the last one turns the graph into agent behaviour you probably do not need all ten start with the layer your current system is missing bookmark this
Gyomei@Gyome1_

x.com/i/article/2080…

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ELG
ELG@elg_oleksandr·
@quaramly this is really useful for me
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Quaramly
Quaramly@quaramly·
ONE VOICE COMMAND JUST REPLACED WHAT A $10,000 AD AGENCY USED TO DO A marketer didn't open Photoshop, brainstorm headlines, or spend hours designing campaigns. He simply said, "Make me 5 ads for Red Bull Summer Edition" and an AI agent handled everything from there Claude Code pulled the brand guidelines, studied competitor campaigns, built a strategy, and generated five finished creatives through Higgsfield without a single manual edit AI read the company's entire brand knowledge base, analyzed competing ads, identified winning creative patterns, and produced campaign-ready assets in minutes. The process that normally involves strategists, copywriters, designers, and creative reviews became one continuous automated pipeline For years, launching five custom ad creatives could easily cost $2,000-$10,000 through an agency. Now the competitive advantage isn't who can afford the biggest creative team, it's who can build the smartest AI workflow first
Quaramly@quaramly

ONE PERSON IS NOW DOING THE WORK OF AN ENTIRE COMPANY Behind the futuristic Jarvis dashboard wasn't a magical AI assistant, it was a team of specialized agents working together inside Slack. One handled customer support, another investigated bugs, another wrote code and kept projects moving while the owner simply approved the final decisions Building a business like this meant hiring support reps, developers, project managers and operations staff before you could even think about scaling. Today, AI agents can take over much of that workflow, allowing one founder to operate with the speed of a much larger team The companies that grow fastest over the next few years may not be the ones with the most employees, but the ones with the best AI systems

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Sethian
Sethian@theSethian·
@quaramly do all five ads still feel like the same campaign side by side
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Аnnetta
Аnnetta@KryptoBestiya·
HE MADE OVER $51,000 BY TRADING TOMORROW'S WEATHER INSTEAD OF TODAY'S HEADLINES ON @Polymarket he's placed just 1,500+ predictions while maintaining an incredible 99% win rate the strategy isn't chasing 1,000%+ moonshots instead, he focuses on consistent, repeatable edges: • allocate around $300–$700 per position • target markets priced between 30¢ and 90¢ • consistently lock in 10%–500% returns his profit curve tells the story he started with almost nothing and steadily compounded his bankroll to over $28.5k profit in the past month with $51k+ all-time
Аnnetta tweet media
Аnnetta@KryptoBestiya

ONE DAY: $4,637 IN PROFIT. ONE MONTH: OVER $10,000. WEATHER TRADING ON @Polymarket. Strategy: a large volume of small bets ($1–3) on contracts with very low "Yes/No" odds (1–6¢), essentially outcomes the market considers unlikely. When one of those long shots hits, the payout is wildly disproportionate to the stake. At the same time, there are bigger positions ($1,000–4,800) in contracts closer to 50/50. Lower ROI, but bigger absolute profit. The approach is diversification across two tracks: Small "lottery-style" bets with minimal risk and outsized potential ROI. Positions on more realistic temperature ranges, where the weather moving in that direction delivers steady, smaller gains. Top hits: $1.92 → $320.20 (+16,566%) $0.81 → $82.10 (+10,041%) $2.97 → $270.00 (+8,990%) There's even a "no-weather" bet in the mix, on Colombia to win the 2026 World Cup ($3,500 → $4,500). Proof the trader isn't married to one category. Just hunting for mispriced markets, wherever they show up.

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biektropic
biektropic@biektive·
NVIDIA's DGX Spark makes local AI look less like a benchmark and more like infrastructure. Cloud gives you access. A local box gives you: > private files stay on the machine > embeddings run next to the data > agent loops stop burning cloud credits > experiments become reusable infrastructure The point is an AI workstation you own.
Lummox@Lummox_eth

x.com/i/article/2074…

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ELG
ELG@elg_oleksandr·
Polymarket's Amazing Move: $1076 - $57426 This guy did only one thing: correctly bet that LeBron James' next team would be the Philadelphia 76ers. Final Report: +$56.4k Yes Philadelphia 76ers (+5,240%) +$9.2k No Miami Heat (+58.6%) +$8.5k No Cleveland Cavaliers (+131.4%) +$883 No Golden State Warriors (+5.3%) All other "yes" positions only lost a combined -$2.3k. Total Profit: +$72.6k. His Wallet: @bigrabbit?via=Fengg" target="_blank" rel="nofollow noopener">polymarket.com/zh/@bigrabbit?… Almost all the profit came from that one large bet on "the 76ers will sign LeBron." He didn't chase the two biggest favorites, Miami and Cleveland, nor did he spread his bets across dozens of outcomes; instead, he placed almost all his chips on a single scenario. Today, all the mainstream traders were wrong. Only he was right.
ELG tweet media
ELG@elg_oleksandr

US x Iran ceasefire by when? Polymarket has the answer $3M volume > July 24: 3% > July 31: 14% > August 14: 34% > August 31: 53% crowd says ceasefire comes but not soon WSJ: Trump is losing patience over an Iran war with no clear end in sight market heard that July 24 crashed to 3% August 31 sitting at 53% > and when matters for oil > when matters for crypto > when matters for everything watch this market it knows before the press conference does

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