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Anant 🔱

Anant 🔱

@Oh_anant

AI Developer

Noida Katılım Şubat 2026
46 Takip Edilen24 Takipçiler
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Anant 🔱
Anant 🔱@Oh_anant·
New challenge unlocked 🔓 I'm spending 1 hour a day on each of these for next 30 Days: → n8n (automation) → LangChain (LLM frameworks) → Claude (AI development) And I'll be posting what I learn every single day — the good, the messy, and the mindblown moments 🤯 Let's see where this goes, let's go! #AI #n8n #LangChain #Claude #BuildInPublic
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Anant 🔱
Anant 🔱@Oh_anant·
Everyone wants to learn AI fast. But no one talks about the days when: → Nothing clicks → You feel stuck → You still show up That’s the real grind. Still building. 💻 #AI #BuildInPublic #100DaysOfCode
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Anant 🔱
Anant 🔱@Oh_anant·
Day 15 of learning n8n 🛠️ Biggest build day yet. Real integrations + a full Lead Management System. 📊 Google Sheets + Notion → Read, write, update & filter records → Built a live data sync workflow between both 💬 Slack + Telegram → Send messages & receive commands → Post formatted cards with buttons → Two-way communication in automations 🔄 Execute Workflow node (revisited) → Call one workflow from another → Built reusable "function" workflows → Lego bricks snapping together 🎯 Built: Lead Management System 4-step end-to-end pipeline: 1️⃣ Webhook — receives new lead 2️⃣ API enrichment — adds data to the lead 3️⃣ Notion CRM — lead saved automatically 4️⃣ Welcome email — sent instantly No code. No manual entry. No dropped leads. This is the kind of system agencies charge thousands to build. I built it in n8n on Day 15. 💪 Day 16 tomorrow 🚀 #n8n #Automation #BuildInPublic #LearningInPublic #WorkflowAutomation #CRM #NoCode #100DaysOfAI #OpenToWork @n8n_io @SlackHQ @NotionHQ
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Sara Hooker
Sara Hooker@sarahookr·
If your a rising AI researcher or builder, join us for 6 months as an ambassador. ✨ We will support grants for community events, credits to build and cohort events. The future belongs to the adaptive.
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Anant 🔱
Anant 🔱@Oh_anant·
Day 14 of learning n8n 🛠️ Moving from linear sequences → modular systems. Today: sub-workflows & the Execute Workflow node. Think of it like moving from one long messy script → clean, reusable functions. 🏗️ 3 core concepts: → Parent Workflow — the orchestrator, calls sub-workflows & waits for results → Sub-Workflow — does ONE thing well, doesn't care who calls it → Execute Workflow node — the bridge. Sends data in, gets result out 🔧 Built today: "Currency Formatter" sub-workflow 4 steps: 1️⃣ Execute Workflow Trigger — makes it callable 2️⃣ Code/Set node — formats raw number to $1,234.00 3️⃣ Last node — returns result to parent 4️⃣ Saved as [SUB] Currency Formatter 💡 The Reusability Rule: Build a sub-workflow when you're: ❌ Copy-pasting 3+ nodes across workflows ❌ Scrolling forever in one workflow ❌ Fixing the same logic in multiple places Pro tip: treat sub-workflows like Lego bricks. Build • Notification • Data Cleanup • Error Logging bricks New automations = just snapping bricks together. Day 15 tomorrow 🚀 #n8n #Automation #BuildInPublic #LearningInPublic #WorkflowAutomation #100DaysOfAI #NoCode #OpenToWork
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Ben Burtenshaw
Ben Burtenshaw@ben_burtenshaw·
you can now just let the ml-intern research papers, process data, write scripts, and start training runs. all on @huggingface
Aksel@akseljoonas

Introducing ml-intern, the agent that just automated the post-training team @huggingface It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on hf.co/spaces, watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and hf.co/papers, reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on hf.co/datasets - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: github.com/huggingface/ml… Web + mobile: huggingface.co/spaces/smolage… And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.

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Anant 🔱
Anant 🔱@Oh_anant·
From simple webhooks to full-scale Data Syncing—my @n8n_io journey is leveling up! 🚀 This week's build highlights: 🎯 Smart Routing: Used the Switch Node to triage data instantly. 📦 Batch Processing: Mastered loops to handle 100+ items without a sweat. 🔄 Live Sync: Connected Google Sheets + Notion with "Search & Update" logic (No more duplicates!). Automation isn't just about moving data; it's about building systems that think. 🧠⚡️ #n8n #BuildInPublic #Automation #NoCode
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Anant 🔱
Anant 🔱@Oh_anant·
I just taught my @n8n_io workflow how to handle the heavy lifting! 💪 Processing 100+ items at once used to be a risk for timeouts and API limits. Now, I’m using: 🔄 Split In Batches to break data into bite-sized pieces. ➰ Looping Logic to process, save, and repeat automatically. 🛑 Done Branch to notify me when the job is finished. Scaleable, stable, and smooth. ⚡️ #n8n #Automation #LowCode #DataEngineering
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Anant 🔱
Anant 🔱@Oh_anant·
My automation just got a brain upgrade! 🧠⚡️ Moving past simple webhooks and into Conditional Branching with @n8n_io. Using the Switch Node to route data like a pro: 🔴 High Priority? Straight to Slack. 🟡 Medium? Logged in Google Sheets. 🟢 Low? Queued for later. Building smarter, not harder. #n8n #BuildInPublic #LowCode #Automation
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Anant 🔱
Anant 🔱@Oh_anant·
D13 n8n🤖 Stop manually copying data from your forms! 🛑 I just built a fully automated Webhook Form Handler using @n8n_io. Here is the flow: 1️⃣ Webhook catches the data instantly. 2️⃣ Filter validates the fields (no more junk!). 3️⃣ Google Sheets logs the entry automatically. 4️⃣ Gmail sends a personalized thank you. Low-code is a superpower. ⚡️ my git repo - github.com/0anant/n8n-wor… #n8n #Automation #BuildInPublic #Productivity
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Anant 🔱
Anant 🔱@Oh_anant·
Day 6 of learning LangChain 🦜 Today: all about Prompts — the interface between you and your LLM. 📝 Types of prompts: → Basic text vs Multimodal (text + images + audio + video) → Static — full prompt changes every query → Dynamic — template-based, only ask user for key details 🧩 PromptTemplate Why use it over plain f-strings? ✅ Built-in validation ✅ Reusable across your codebase ✅ Native LangChain ecosystem fit ✅ Works directly in chains from langchain_core.prompts import PromptTemplate 💬 3 Message types in LangChain: → SystemMessage — sets AI behavior & persona → HumanMessage — user input → AIMessage — model response from langchain_core.messages import SystemMessage, HumanMessage, AIMessage 🔄 MessagePlaceholder A special placeholder inside ChatPromptTemplate to dynamically insert conversation history or a list of messages at runtime. This is how you build memory-aware chat apps in LangChain. Day 7 tomorrow 🚀 #LangChain #LLM #PromptEngineering #BuildInPublic #LearningInPublic #AI #100DaysOfAI #OpenToWork
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Anant 🔱
Anant 🔱@Oh_anant·
Day 5 of the LLM course 🧠 @huggingface Deep dive into the 3 Transformer architectures — and finally understanding WHEN to use each. 📘 Encoder-only (BERT) → Bidirectional attention — sees ALL words simultaneously → Pretraining: masked language modeling (predict masked words) → Best for: classification, NER, extractive QA → Understanding tasks, not generation 📗 Decoder-only (GPT, Llama) → Unidirectional — only sees words BEFORE current position → Pretraining: predict next token → Best for: text generation, code, reasoning, few-shot learning → Where ALL modern LLMs live → Trained in 2 phases: pretraining → instruction tuning 📙 Encoder-Decoder / Seq2Seq (T5, BART) → Encoder sees full input, decoder generates output → Pretraining: reconstruct corrupted/masked text spans → Best for: translation, summarization, generative QA, data-to-text 🎯 The decision framework: → Need to UNDERSTAND text? → Encoder → Need to GENERATE text? → Decoder → Need to TRANSFORM text? → Encoder-Decoder ⚡ Bonus: Attention efficiency Standard attention = O(n²) complexity Longformer & Reformer use sparse attention to handle long sequences Day 6 tomorrow 🚀 @abidlabs @ben_burtenshaw @carrigmat @LysandreJik @mervenoyann @_lewtun @lvwerra #HuggingFace #Transformers #LLM #NLP #ComputerVision #BuildInPublic #LearningInPublic #AI #100DaysOfAI #OpenToWork #Claude #AI #AIAutomation @ChatGPTapp @claudeai @n8n_io @LangChain
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Anant 🔱
Anant 🔱@Oh_anant·
🚀 I spent the last few weeks learning AI + Automation (1 hr/day)… Most people are just using AI tools. I learned how to build systems with them. Here’s what I covered: ⚙️ n8n → Automation systems 🤖 Claude → AI application building 🧠 Transformers → how AI actually works 🔗 LangChain → turning LLMs into products 💡 Biggest realization: AI is NOT about prompts. It’s about: 👉 Data flow 👉 Context 👉 Memory 👉 System design ⚡ What I now understand: • n8n = ETL pipelines for automation • Transformers = foundation of all modern AI • Claude API = real-world AI apps • LangChain = architecture for AI systems 🛠️ What I can build now: • AI-powered apps • Automation workflows • Chat systems with memory 🧠 The shift: Beginner → “write better prompts” Builder → design better systems 🔥 Currently building in public: n8n • Claude • LangChain • Hugging Face If you're building with AI — let’s connect. #AI #AIAutomation #LangChain #Claude #Transformers #n8n #BuildInPublic #OpenToWork @ClaudeDevs @claudeai @huggingface @LangChain @n8n_io
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Nandini
Nandini@N_and_ni·
I want to share something real… I started from 0. I was the one taking screenshots, overthinking every post. Today I’m at 4K. Still not where I want to be… but I’ve learned a lot. Lately, I’ve been getting a lot of DMs: “Didi, I’m stuck at 200…” “I’m stuck at 1K…” So here’s what I want to tell you: • Keep posting. Don’t stop. • Keep replying. That’s where growth happens. But Nandini , what to post ? 🤔 • Post everything your POTD, your thoughts, even small moments of your day. • You never know what will go viral. Here’s something most people don’t realize: Out of my 4.6K connections, only 1.6K are verified. And I don’t follow people just by looking at profiles. I follow based on: • How you reply • What you post • How you show up consistently If it feels real and valuable → I hit follow. And trust me, it’s not just me… that’s how most people grow. Now the most important part: Give it 3 months. • Promise yourself: – 3 posts/day – 20+ quality replies/day Save this tweet. Come back after 3 months. And see the difference yourself. No shortcuts. Just consistency. See you on the other side, future creators 🤗
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Kevin
Kevin@KevinPicchi·
Managed to find a list of 2500 VCs (They specialize in AI & SaaS) Never been a better time to get funded Want the list? Comment "VC" and LIKE this post. Will DM in 24 hours.
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laura
laura@lauradang0·
I’m going to start a partnerships/growth group chat for startups! DM if you wanna join :))
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