Grootex @Grootexbt
Amsterdam, The Netherlands Joined September 2026-
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я сделал эту игру при помощи Claude Code за считанные минуты
here's how to build a live trait scanner like this with Claude Opus 5.5... the trick is treating a Pudgy Penguin like a lab subject, not a static NFT card, but there's a smart way to set it up: - step 1: lock one subject and one pipeline: hair, eyes, medal, full scan, then the grade - step 2: write the trait dictionary first so Opus never invents parts that are not on the body - step 3: build the center stage: the penguin, the aim reticle, the FULL SCAN badge - step 4: add the left read as traits only: afro, pick, eyes, beak, fur, medal, ribbon, aura - step 5: add the right read as scan stats only: conf, depth, rate, lock, sway - step 6: wire the lower deck last: star path, crystal ball, rune track, mana curve - step 7: play the scan end to end and fix one panel at a time until the lock feels real who does what: > Opus: the UI, the data, the step logic > you: the subject, the trait list, the pass / fail taste and the two rules that make it work... one step owns one read the verdict never runs before the full silhouette is locked start with one penguin, then the next scan is just a new subject in the same machine
AI agents are getting f...cking good at building other AI agents 10 GitHub repos for turning GPT 6 Astra into an AI engineering lab that generates training data, improves models, and tests what actually works 01 DSPy ▸ github.com/stanfordnlp/ds… → optimize prompts and AI pipelines programmatically GENERATE THE TRAINING DATA 02 Distilabel ▸ github.com/argilla-io/dis… → generate and process synthetic datasets with LLMs 03 DataDreamer ▸ github.com/datadreamer-de… → build reproducible synthetic data workflows 04 Argilla ▸ github.com/argilla-io/arg… → collect human feedback and curate datasets TRAIN THE SPECIALISTS 05 Unsloth ▸ github.com/unslothai/unsl… → accelerate fine tuning with optimized training workflows 06 LLaMA Factory ▸ github.com/hiyouga/LLaMA-… → fine tune and evaluate open models 07 Axolotl ▸ github.com/axolotl-ai-clo… → configure and run model training pipelines TEST WHAT ACTUALLY IMPROVED 08 lm-evaluation-harness ▸ github.com/EleutherAI/lm-… → benchmark models across standardized tasks 09 OpenCompass ▸ github.com/open-compass/o… → evaluate model capabilities across multiple benchmarks DEPLOY THE WINNERS 10 BentoML ▸ github.com/bentoml/BentoML → package and serve models as production services the architecture: GPT 6 Astra → generate data → curate → fine tune → benchmark → compare → deploy → collect feedback → repeat 3 builds worth bookmarking: coding specialist: DSPy → Distilabel → Unsloth → lm-evaluation-harness research specialist: DataDreamer → Argilla → LLaMA Factory → OpenCompass production lab: Distilabel → Axolotl → lm-evaluation-harness → BentoML here's the part most people miss you don't need your most powerful model answering every question you can use it to help create the data and workflows that make smaller models useful for specific jobs one powerful model helping build an entire fleet of specialists that's a much more interesting use of intelligence ⭣
i left GPT-6 Astra alone with a 42-task queue and full write access overnight then at 7:00 i had 42 completed receipts and almost nothing i could ship every receipt looked legitimate handoffs were clean timestamps were perfect the queue was empty but the folder was full of plans about plans agents had rewritten AGENTS.md twice reorganized research/ three times and produced 6 new review checklists for work that never happened the system hadn’t stalled it had learned how to look finished i dug into decisions.jsonl and the pattern was obvious whenever a task was slightly ambiguous, the safest move wasn’t to ask me it was to create another artifact that counted as progress so i changed the definition of done no receipt counted unless it pointed to a final artifact outside the planning layer next night, same queue shape, same agents 17 tasks finished 25 stayed blocked on purpose and the folder finally had the files i actually needed autonomy without a hard definition of finished doesn’t create speed it creates beautiful busywork at machine scale
GPT 6 Astra needs more than a chat box 9 GitHub repos turn it into a builder that codes and uses the web 01 Aider ▸ github.com/Aider-AI/aider → pair programs in the terminal and commits the diff 02 Continue ▸ github.com/continuedev/co… → keeps the coding agent inside the IDE 03 browser-use ▸ github.com/browser-use/br… → drives a real browser instead of guessing the UI 04 Crawl4AI ▸ github.com/unclecode/craw… → turns live sites into clean LLM ready Markdown 05 MarkItDown ▸ github.com/microsoft/mark… → converts files and office docs into Markdown context 06 Composio ▸ github.com/ComposioHQ/com… → gives authenticated tools without building every connector 07 Daytona ▸ github.com/daytonaio/dayt… → runs generated code in an isolated workspace 08 promptfoo ▸ github.com/promptfoo/prom… → tests prompts and agent paths before you trust them 09 Phoenix ▸ github.com/Arize-ai/phoen… → shows traces when a run fails in production Astra should spend tokens on judgment calls The browser, files, tools, sandbox, evals and traces stay in dedicated layers Swap the model tomorrow and most of this stack still holds
GPT 6 Astra + GitHub is getting ridiculous 8 open source repos turn it into a real coding system 01 LangGraph ▸ github.com/langchain-ai/l… → stateful loops for long coding jobs 02 Docling ▸ github.com/docling-projec… → turns PDFs and office docs into text 03 Crawl4AI ▸ github.com/unclecode/craw… → scrapes live docs into markdown 04 Qdrant ▸ github.com/qdrant/qdrant → retrieves the right repo chunks 05 Graphiti ▸ github.com/getzep/graphiti → keeps project knowledge over time 06 FastMCP ▸ github.com/PrefectHQ/fast… → turns local tools into MCP servers 07 smolagents ▸ github.com/huggingface/sm… → agents that write actions as code 08 Phoenix ▸ github.com/Arize-ai/phoen… → shows what broke in a failed run Astra should spend tokens on the hard judgment calls Docs, retrieval, memory, tools and traces stay in dedicated layers bookmart the list of paper right now
i stopped wasting Opus on tasks Haiku can handle 8 GitHub repos for building a Claude Code agent tree where expensive models think and cheaper agents do the grunt work 01 anthropics/claude-agent-sdk-python ▸ github.com/anthropics/cla… → programmatic subagents, isolated contexts and sessions for coordinating specialized workers ONE MODEL DOES EVERYTHING 02 wshobson/agents ▸ github.com/wshobson/agents → 202 specialized agents with model selection across coding, debugging, research and security 03 anthropics/claude-code ▸ github.com/anthropics/cla… → the coding agent runtime that handles files, commands, tools and subagent delegation THE PLAN FALLS APART 04 obra/superpowers ▸ github.com/obra/superpowe… → turns vague coding requests into implementation plans with testing and review checkpoints 05 anthropics/skills ▸ github.com/anthropics/ski… → reusable instructions and scripts that load when needed instead of bloating every agent’s context WORKERS CAN’T TOUCH REAL SYSTEMS 06 modelcontextprotocol/servers ▸ github.com/modelcontextpr… → reference MCP servers for connecting agents to external tools and data 07 anthropics/claude-code-action ▸ github.com/anthropics/cla… → runs Claude Code inside GitHub Actions so repository workflows can trigger coding tasks NOBODY KNOWS WHAT THE AGENTS DID 08 langfuse/langfuse ▸ github.com/langfuse/langf… → traces agent execution, tool calls and model usage so you can inspect failures and costs the architecture from the video is the part worth understanding Opus handles difficult decisions and architecture Sonnet coordinates implementation and task execution Haiku handles smaller focused jobs like exploration and research workers return their findings to the higher reasoning layer for review the trick is not spawning 20 agents and hoping they cooperate it’s matching task complexity to model cost, isolating context and verifying the result before the next task begins one expensive model doing everything is a terrible default when you can build an entire execution tree around it
Bookmarked this list of GitHub repos
AI data infrastructure is getting f…cking illegal 10 GitHub repos for building a GPT 6 Astra analyst that can investigate millions of records without stuffing everything into one prompt 01 ClickHouse ▸ github.com/ClickHouse/Cli… → analytical queries across massive datasets
AI data infrastructure is getting f…cking illegal 10 GitHub repos for building a GPT 6 Astra analyst that can investigate millions of records without stuffing everything into one prompt 01 ClickHouse ▸ github.com/ClickHouse/Cli… → analytical queries across massive datasets FIND THE RIGHT DATA 02 Trino ▸ github.com/trinodb/trino → query multiple data sources through one SQL engine 03 Apache Iceberg ▸ github.com/apache/iceberg → manage huge analytical tables with reliable snapshots MAKE THE DATA SEARCHABLE 04 Qdrant ▸ github.com/qdrant/qdrant → semantic search across unstructured information 05 LanceDB ▸ github.com/lancedb/lancedb → vector search with multimodal data support 06 Tantivy ▸ github.com/quickwit-oss/t… → fast full-text search for precise evidence retrieval GIVE AI A QUERY ENGINE 07 SQLGlot ▸ github.com/tobymao/sqlglot → parse, transform, and validate SQL syntax 08 Ibis ▸ github.com/ibis-project/i… → write portable analytics across different backends TURN QUERIES INTO EVIDENCE 09 Apache Arrow ▸ github.com/apache/arrow → move columnar data efficiently between systems 10 Marimo ▸ github.com/marimo-team/ma… → build reactive Python notebooks and interactive analysis apps the architecture: question → discover → retrieve → generate SQL → validate → execute → analyze → explain 3 builds I’d actually explore: market intelligence: Trino → ClickHouse → Marimo company knowledge: Qdrant → Tantivy → LanceDB research analytics: Iceberg → Ibis → Arrow → Marimo the interesting shift is what happens when GPT 6 Astra stops carrying the entire dataset in its context window the data stays where it belongs the model figures out what to ask 10 million rows don’t need to become 10 million tokens ⭣
GPT 6 Astra + GitHub is getting really dangerous… 7 open source repos are enough to build a serious coding system around it 01 OpenHands ▸ github.com/OpenHands/Open… → gives Astra an environment for editing code, running commands and working through real engineering tasks 02 Serena ▸ github.com/oraios/serena → understands symbols, references and repo structure before the model starts changing files 03 Context7 ▸ github.com/upstash/contex… → pulls current docs and examples when the task depends on external libraries 04 GitHub MCP Server ▸ github.com/github/github-… → gives the agent direct access to issues, repos and GitHub actions 05 E2B ▸ github.com/e2b-dev/E2B → runs generated code and commands inside an isolated environment 06 pytest ▸ github.com/pytest-dev/pyt… → turns the existing test suite into a hard gate before completion 07 Langfuse ▸ github.com/langfuse/langf… → shows exactly what the model saw, called and broke during the run what I’d actually give each layer repo understanding → Serena current docs → Context7 engineering workspace → OpenHands GitHub access → GitHub MCP runtime → E2B verification → pytest debugging the agent itself → Langfuse GPT 6 Astra sits above all of it and handles the decisions that actually need reasoning That is a much better use of a frontier model than making it waste context rediscovering files, reading stale docs and guessing why its own code failed
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i gave GPT-6 Astra 32 agents one task and banned them from agreeing too early then in 11 minutes they produced 68 competing decisions, killed 51 of them, and still couldn’t agree on the final answer that was exactly what i wanted the strongest agents weren’t the ones producing more ideas they were the ones finding reasons other agents were wrong bad sources got killed weak assumptions got killed confident answers with no evidence got killed by the final round, only 3 paths were left then i forced those 3 to attack each other again one survived i used to think better AI meant getting the right answer faster now i’d rather build a system where the wrong answer has to survive 67 chances to die
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AI data agents are getting f...cking illegal 10 open-source GitHub repos for turning GPT 6 Astra into a research analyst that can find data, clean it and build reports 01 SQLCoder ▸ github.com/defog-ai/sqlco… → turn natural language into SQL CONNECT THE DATA 02 Airbyte ▸ github.com/airbytehq/airb… → sync data from APIs, databases, and services 03 dlt ▸ github.com/dlt-hub/dlt → build Python data pipelines without managing complex infrastructure MAKE THE NUMBERS TRUSTWORTHY 04 Great Expectations ▸ github.com/great-expectat… → catch missing values, broken schemas, and invalid data 05 Soda Core ▸ github.com/sodadata/soda-… → automate data quality checks 06 OpenMetadata ▸ github.com/open-metadata/… → discover datasets, owners, and data lineage LET AI EXPLORE THE DATA 07 Dataherald ▸ github.com/Dataherald/dat… → generate SQL from questions about structured data 08 PyGWalker ▸ github.com/Kanaries/pygwa… → explore datasets through interactive visualizations TURN RESULTS INTO REPORTS 09 Apache Superset ▸ github.com/apache/superset → build dashboards from SQL data 10 Quarto ▸ github.com/quarto-dev/qua… → turn analysis into reproducible reports the architecture: sources → ingestion → validation → metadata → SQL → analysis → visualization → report 3 builds worth bookmarking: market research: Airbyte → Great Expectations → SQLCoder → Quarto business intelligence: dlt → OpenMetadata → Dataherald → Superset data exploration: dlt → Soda Core → PyGWalker → Quarto the real advantage isn't asking GPT 6 Astra to guess what your numbers mean it's giving the model a system that can retrieve the actual data, check it, and show the evidence AI stops guessing when the data starts talking ⭣
AI agents are getting f...cking dangerous with real data 9 GitHub repos for building a GPT 6 Astra system that can explore databases, write SQL, run analysis, and turn raw numbers into decisions 01 Vanna ▸ github.com/vanna-ai/vanna → natural language questions into SQL queries GIVE AI ACCESS TO DATA 02 DB-GPT ▸ github.com/eosphoros-ai/D… → build AI applications around private databases 03 MindsDB ▸ github.com/mindsdb/mindsdb → connect AI workflows to enterprise data MAKE THE DATA USABLE 04 DuckDB ▸ github.com/duckdb/duckdb → run analytical SQL directly on local files 05 Polars ▸ github.com/pola-rs/polars → process large datasets with fast DataFrame operations 06 DataFusion ▸ github.com/apache/datafus… → build custom SQL query engines TURN ANALYSIS INTO ACTION 07 PandasAI ▸ github.com/sinaptik-ai/pa… → ask questions about datasets in natural language 08 Evidence ▸ github.com/evidence-dev/e… → turn SQL queries into interactive reports 09 Metabase ▸ github.com/metabase/metab… → explore metrics and build dashboards the architecture: question → schema → SQL → query → validate → analyze → visualize → decision 3 builds worth stealing: finance: Vanna → DuckDB → Evidence business: MindsDB → Polars → Metabase research: DB-GPT → DataFusion → PandasAI here's what makes this interesting most AI agents can explain your business very few can actually inspect the numbers behind it GPT 6 Astra + the right GitHub stack changes what you can build the next useful AI assistant won't just answer questions it'll know where to look for the answer ⭣
i gave GPT-6 Astra 24 agents one problem they produced 41 competing decisions before a single answer was allowed to survive then i made every agent attack the result at first it looked completely unnecessary some agents reached the same conclusion through different evidence others disagreed on tiny assumptions that changed everything downstream a few found answers that looked stronger than the one i would have picked myself so i stopped treating disagreement like noise each decision had to survive another pass one agent challenged the source another challenged the logic another looked for the hidden assumption another tried to break the result with a completely different path weak answers started disappearing fast then the interesting part happened the system stopped improving by adding more ideas it improved by killing bad ones earlier 41 decisions became 17 17 became 6 6 became 2 and those final 2 kept attacking each other until only one was still standing the final answer wasn’t the smartest sounding one it was the one that had survived the most pressure that changed how i think about GPT-6 Astra i used to optimize for better first answers now i’m much more interested in building systems where bad answers simply don’t make it to the end
@Lummox_eth And some people still just watch it and don’t build
holy sh*t, GPT 6 Astra is getting f...cking treasure stop wasting the best model in the stack on work another layer can handle put Astra in charge of decisions let everything else do the mechanical work Serena maps the repo before Astra plans anything Context7 pulls the exact docs before Astra touches an API Browser Use handles the web when the answer is outside the codebase E2B runs the code in a clean sandbox pytest decides if the patch actually survives Langfuse keeps the trace when something goes wrong Astra thinks the stack executes and the model only wakes up when the path actually needs reasoning → before a multi file change, Astra decides what should move and what should stay untouched → after a failed run, Astra gets the error and chooses whether to change the code, the environment, or the plan → before the PR, Astra checks the final diff instead of blindly trusting a green test the full setup > GPT 6 Astra handles planning, code decisions and final review > Serena handles symbols, references and repo structure > Context7 handles current docs and library examples > Browser Use handles missing external context > E2B handles execution and dependency installs > pytest handles the hard pass or fail gate > Langfuse keeps every model call and tool action visible the loop becomes issue → map repo → load docs → plan → edit → run → test → inspect failure → fix → review diff → PR paste this into your coding agent "Build this repository workflow around GPT 6 Astra Use Serena before planning any code change so the model sees the relevant symbols, references and affected files Use Context7 before touching external libraries or APIs Use Browser Use only when required information is outside the repository or documentation context Run all code, installs and commands inside E2B Use pytest as the final verification gate Send failed execution output back to GPT 6 Astra for replanning Trace the entire run with Langfuse GPT 6 Astra should handle reasoning, architecture decisions, difficult debugging and final diff review Do not spend model calls on mechanical file lookup, documentation retrieval, command execution or test running Follow this order every time inspect → research → plan → edit → execute → test → repair → review → PR"
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Dailygistroom @DailygistroomHQ
4K Followers 6K Following Real-time breaking news, entertainment trends, & global sports highlights. Stay ahead of the timeline with unfiltered stories. 📧 [email protected]
Bet105 @bet_105
19K Followers 15K Following Global Crypto Sportsbook | Reduced Juice | Winners Welcome | Instant Payouts💰
Markdon78 @markdon78
8K Followers 6K Following #Gate Ambassador || content creator || Moderator || Brand Ambassador || MOD || Marketing Strategist|| Graphic designer & stats || shiller || DM for collabs
Thuy Signals (✱,✱... @thuykeo56
29K Followers 32K Following Building & learning in Web3 daily. Ambassador @StandX_Official @axisrobotics @BitRobotNetwork
Coolassusername @roodio
34K Followers 32K Following The world's most interesting man ⓘ 𝗧𝗵𝗶𝘀 𝗰𝗹𝗮𝗶𝗺 𝗶𝘀 𝗱𝗶𝘀𝗽𝘂𝘁𝗲𝗱. IFB 💯 Twitter removed my verified blue checkmark ☹️ #SkyFamDrive
🆂🅽🅾🆆 @SnowMan_124
5K Followers 3K Following web3 creator || God’s way || Degens ✦ building with @skillstkr ✦ Growth Strategy ~ @AUREUSONBASE ✦Social Media Manager ~ @INFRACOINN
ladyblognews @ladyblognewss
62K Followers 2K Following Your best source for everything related to Miraculous. Watch Season 6 on https://t.co/MJffIztRP0 All rights reserved to Miraculous Corp #MLBS7Spoilers
Matthew Gideon @gideonsportnews
7K Followers 4K Following Katie’s Husband. Sports journalist. https://t.co/3SNCgFjEJg. #Packers #CBJ #Bucks #Syracuse Jewish. Liberal.
Lily Rose @LilyRose10u
9K Followers 9K Following Coffee lover ☕ | Always asking questions | 🌸 Dreamer | 🌷 Here for Good Vibes & New Friends | 💖Textile Engineer | 🚫 No Porn |
TMM ID @tmm_id
2K Followers 2K Following
✗ @5MMMJ
28K Followers 26K Following ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀ ⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
W 🎖️ @WickedFi
23K Followers 22K Following Web3 | KOL | NFT | Trader | Marketing Manager Building. Trading. Exploring the future. DM for collaborations. AI Builder | AI Agents
ִֶָ 𝐌𝐨𝐚�... @5554P
24K Followers 22K Following ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀ ⠀ ⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀
M_JUMA👻 @Jumane22
14K Followers 14K Following s m c trader | web3 researcher | promoter | ambassador | @tryquantio | @MEXC_AfricaBU | DM for callab
Zunnu @ZunnuMetaX
42K Followers 30K Following KOL || NFT Explorer || Content Writer || Marketing Manager || CMC & Binance Square Verified / ED_KISuT7 Trader @AlphaX_DEX || DM for Collab ↓
𝒮ℋℰℰ𝒦𝒪... @sheekoo16100
8K Followers 7K Following ۞ وآن ضـاقــت بِــگُ الأرگــانُ يَـومـاً فـرگـُنُ اللـةٍ لـآ يضـَيـُق 🩵
Haneen Murtaza @HaneenMurtaza_
11K Followers 10K Following In my crypto era — learning • Researching • Building
KHAN @Khanu_96
6K Followers 6K Following
7222P @7222P
25K Followers 20K Following ⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
么Ammar么 @__K8R8
22K Followers 19K Following
ᘓɹ̈᎗ɹ̇ȷ�... @78l87
19K Followers 17K Following ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀ ⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
SF ZOGEX @SFZOGEX69
12K Followers 12K Following 🌐 Web3 Promoter | 🤝 Ambassador 📣 Project Promotion • 📈 Community Growth 🚀 Open for Collaborations & Partnerships 📩 DM for Collabs
Stick @stick_web3
16K Followers 14K Following Building in Web3 ⚡ Growth • Community • Partnerships 🤝 For projects & collaborations — DM 📩
Livre Pensador 🧠 @Lunar92Silva
6K Followers 6K Following "Quem tem a mente livre nunca estará trancado, não importa o tamanho dos muros."
Jacob 🐍 @hoanhphuc01
6K Followers 7K Following Community is everything .Content Creator , Web3 Researcher | AI • DeFi • RWA • Infrastructure.
- Al Hughes - ANTI K ... @hughes_per23074
14K Followers 14K Following TURFMAM 🐎🐎🐎 FOLLOW BACK COGITO,ERGO SUM
Berkan Ağa @berkanagaa
27K Followers 25K Following "Hiçbir zafere çiçekli yollardan gidilmez, 🥀 I follow back.💯 takiplere geri dönüş yapılır 🪻
𝑹𝒊𝒏𝒂 𝑵... @Rina_network
15K Followers 14K Following 🎬 𝐂𝐫𝐞𝐚𝐭𝐨𝐫 𝐆𝐫𝐨𝐰𝐭𝐡 𝐇𝐮𝐛 📊 𝐑𝐞𝐚𝐜𝐡 • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 • 𝐌𝐨𝐧𝐞𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧 📣 𝐏𝐫𝐞𝐦𝐢𝐮𝐦 𝐏𝐫𝐨𝐦𝐨𝐬 & 𝐒𝐡𝐨𝐮𝐭𝐨𝐮𝐭𝐬
Victoria Scott $ @ASM_Dish
25K Followers 14K Following Crypto | Web3 | Shoutouts 🤝Supporting builders, projects, small brands, creators & influencers 🌐 DM open for promos & collaborations Only accept crypto 🤑💵
Muktar Garma🎖️ @GarmaMuktar
5K Followers 4K Following Football 🏐 Lovers and a Motivational Speaker 🔊 writer 🖊️
AW-CRYPTO @AsiaWhales
12K Followers 7K Following Living on Web3 | Reply Guy | Crypto lover since 2018 | Onchain explorer | Sharing insights on DeFi & NFTs | DM FOR COLLAB
Lummox @Lummox_eth
8K Followers 398 Following developing internet money / building interesting things / dm open
Garry Tan @garrytan
1.1M Followers 6K Following President & CEO @ycombinator —Founder @garryslist—Creator of GStack & GBrain—designer/engineer who helps founders—SF Dem accelerating the boom loop
Marc Andreessen 🇺�... @pmarca
6.7M Followers 34K Following You’re not talking to someone who woke up a loser. That loser attitude, that loser premise makes no sense to me.
Suhail @Suhail
450K Followers 709 Following Starting something new. Past: CEO & Founder @mixpanel Pizzatarian, engineer, music maker
Ryan Hoover @rrhoover
496K Followers 2K Following Founder of @ProductHunt. Investor at @WeekendFund. Married to @SandyBKwon. Hi! 👋🏼
Sam Altman @sama
6.4M Followers 1K Following The mission of OpenAI is to ensure that AGI benefits all of humanity
OpenAI @OpenAI
5.4M Followers 4 Following OpenAI’s mission is to ensure that artificial general intelligence benefits all of humanity. We’re hiring: https://t.co/dJGr6LgzPAYou might like
