Raghav 「 🖐️ ✦ 🌏 」

[Akash @Akasheth_]

Before you build a multi-agent system, learn these concepts first:

• Multi-Agent Systems - https://t.co/snWHV1cWue
• RAG - https://t.co/j95UB1gzwc
• Agentic RAG - https://t.co/vFvJfMoYLl
• MCP - https://t.co/E9rdU5uQkx
• LangGraph - https://t.co/W1EBr3GXcY
• Memory - https://t.co/T9PTVfDpLc
• Evals - https://t.co/mxqG7misC3
• Context Engineering - https://t.co/jM0nkrMS5u

[vitalik.eth @VitalikButerin]

This is why:

* Self-sovereign identity, data and money (so you control your account, not a third-party provider)
* CROPS AI (so other people cannot do this to *your computer* https://t.co/zmG8wrfzAi )

[anand mahindra @anandmahindra]

We still don’t know enough about the full consequences of climate change.

But we do know this: intense heat waves in India are no longer exceptions. They’re becoming a way of life.

These homeowners have responded by changing THEIR way of life.

By changing the way they live, they’ve created homes that are cooler, more sustainable, and productive enough to grow their own food.

That’s the kind of adaptive thinking we’ll increasingly need in the years ahead.

Trailblazers. 👏🏽👏🏽👏🏽

[CryptoTelugu @CryptoTeluguO]

😁Getting close to 500 @arc House points

📸Post ur screenshots

🤔Many asking how I'm earning points

👇Daily
🪙4 video articles - 16pts
🪙5 articles - 10pts
🪙Check-In - 1pt
🪙Total-27 pts

✅Go to home,scroll & load more for older articles & read

⚠️NO REWARDS⚠️

💙Like
🔁RT

[CoinGecko @coingecko]

🦞 OpenClaw AI just might be the new standard in algorithmic trading.

In this article, learn how to pair it with the CoinGecko API to build trading strategies like cross-exchange arbitrage, news-based sentiment trading, and more.

Read the full guide 👇
https://t.co/aeU3adsp54

[CryptoTelugu @CryptoTeluguO]

🪂 @billions_ntwk Airdrop is live

🔗 https://t.co/Adsk7vFr4o
📸Check & post your screenshot

🤣But wait.. There is no TGE unlock
📅Tokens automatically staked till 31st October
🗓️Basically 6mth lock

🔓No wonder in Oct, if they add staking unlock cooling period of 3mths then add vesting for 1 year
😁And later add pay fee to claim ur tokens

🤷Airdrop crime continues
😉And I'm sure this 6mth lock would be suggested by same "Major CEX" that cancelled Token sale TGE unlock

🫠So except Binance no one got TGE tokens

💙Like
🔁RT

[Arcium ☂️ @Arcium]

Solflare RTGs are now live

Six new RTGs with @solflare ↓

[tao.bot (τ, τ) @taodotbot]

🔎 Bittensor $TAO subnet check-in: top 5 by emissions

@TargonCompute (SN4) — Confidential AI compute: focused on running sensitive AI workloads on decentralized hardware. Targon continues to build around Intel TDX / Trust Authority and NVIDIA Confidential Computing, with recent momentum driven by its Intel-linked research and growing demand for private inference.

@lium_io (SN51) — Decentralized bandwidth / infrastructure layer: positioned around enabling network-level throughput and connectivity across the Bittensor stack. It has been steadily maintaining high emissions with consistent usage, suggesting strong underlying demand for its role in the network.

distil (@arbos_born - SN97) — Model distillation + optimization: focused on compressing and improving model efficiency across Bittensor. As more subnets push toward production use, distillation becomes increasingly important for lowering costs and improving deployability.

@webuildscore (SN44) — Evaluation / scoring infrastructure: provides the layer that measures model outputs and performance. As emissions become more tightly tied to measurable results, scoring subnets like SN44 play a critical role in determining where value flows.

ORO (@oroagents - SN15) — Decentralized data / pretraining: focused on large-scale data pipelines and model training across distributed compute. Continues to see strong emissions as data and training remain core primitives of the network.

Across compute (Targon), bandwidth (lium), optimization (distil), evaluation (Score), and data/training (ORO), the top of Bittensor emissions is spread across the full AI stack.

Trade & research subnets

[USD.AI @USDai_Official]

More utilities and features to be added in the coming weeks.

Read the full recap.

[CryptoTelugu @CryptoTeluguO]

🪂 @farcaster_xyz Retriactive Allocation checker is out

🔗 https://t.co/y5oBu925uL
✅Open this in farcaster
✅Click "My Rewards"
✅Now it will show ur estimated rewards
📸Post your screenshot

⚠️This is not final allocation
⚠️This is not from New Sold out farcaster (classic)

✅This is from old farcaster team, they r going to fork & do Genasis
📝I will make detailed post about this "Farcaster" & "Farcaster Classic" cold war soon

🪙Total supply - 2B
🪙Retriactive rewards (Airdrop) - 200M
📆TGE - before 4th May

😉If u remember we were super active during 2024(phaver)
✅If u were active, check u might be eligible for good allocation

🔜Soon detailed post about Farcaster & Classic

💙Like
🔁RT

[Vivo @vivoplt]

The best AI courses that are FREE to learn in 2026:

1. Anthropic: https://t.co/HtLh3xW2Ys
2. Google: https://t.co/Rv9EcJBJSX
3. Meta: https://t.co/y3M9fSfNpn
4. NVIDIA: https://t.co/3yUGRLTHwN (GOATed)
5. Microsoft: https://t.co/EFY1QTAdee
6. OpenAI: https://t.co/8mlAh908HE
7. IBM: https://t.co/9ljNUxFlTU
8. AWS: https://t.co/oTPno3bcMF
9. DeepLearningAI: https://t.co/fQs7wNpUVu
10. Hugging Face: https://t.co/JzfifhrXG1
11. FastAI: https://t.co/KmpKcjY4i7
12. Kaggle Learn: https://t.co/8yQKcfz7tK
13. Stanford AI: https://t.co/YjZOwoPGTT
14. MIT OpenCourseWare: https://t.co/vkgSKvhdgX
15. Full Stack Deep Learning: https://t.co/O5RGXnbiOe
16. DeepMind Resources: https://t.co/WtaxYKwWi4…
17. OpenAI Cookbook: https://t.co/WXGZ2zVfI9…
18. Papers With Code: https://t.co/m5sihToOxU
19. AssemblyAI Blog: https://t.co/pMbKvWD10q
20. Pinecone Learn: https://t.co/uCZP8q3Uvp

Never pay for AI courses.

[Om Patel @om_patel5]

THIS GUY AUDITED 926 CLAUDE CODE SESSIONS AND FOUND MOST OF THE TOKEN WASTE WAS ON HIS SIDE

everyone is blaming anthropic for the limits, so he decided to actually look at the data

858 sessions, 18,903 turns, and $1,619 estimated spend across 33 days

here's what he found:

1\ one default setting was burning 14,000 tokens per turn

Claude Code loads the full JSON schema for every tool into context at session start. whether you use them or not. 20,000 tokens of tool definitions sitting there on every single turn.

the fix: one line in your settings.json

"ENABLE_TOOL_SEARCH": "true"

context dropped from 45K to 20K instantly. across 858 sessions that one setting was wasting an estimated 264 million tokens

2\ cache expiry is the single biggest waste

54% of his turns came after a 5+ minute idle gap.

every one of those turns re-processed the entire conversation at full price which caused a 10x cost jump

you go grab coffee. come back 5 minutes later. type your next message. everything rebuilds from scratch. the context didn't change. you didn't change. the cache just expired.

12.3 million tokens wasted on idle gaps alone

3\ 42 skills loaded. 19 of them used twice or less across 858 sessions.

every one of those skill schemas sat in context on every turn eating tokens for nothing.

4\ 1,122 redundant file reads where the same file was read 3+ times

one session read the same file 33 times.

he ALSO built a full token auditor dashboard that shows you exactly where your waste is coming from

19 charts, opens in your browser, free AND open source

[Virat Singh @virattt]

Our AI hedge fund hit 50,000 stars on GitHub.

It's a team of agents that analyze stocks like the world's best investors.

Has 19+ agents:
• Warren Buffett
• Charlie Munger
• Phil Fisher, etc

Soon it’ll manage your portfolio for you.

[tao.bot (τ, τ) @taodotbot]

📈 Bittensor $TAO Flow Leaders — Weekly Check-In

@gradients_ai (SN56) — +$815.3K / +τ2.63K
Gradients keeps attracting flow as one of the easiest ways to train image and text models on Bittensor. The core pitch is simple: pick a base model, dataset, and training time in a few clicks, then compete in recurring training tournaments.

@TrajectoryRL (SN11) — +$796.7K / +τ2.57K
TrajectoryRL is a decentralized prompt and policy optimization subnet for AI agents. It runs an open competition around improving OpenClaw agent instructions, with the goal of making agents cheaper, faster, and more reliable.

@404gen_ (SN17) — +$678.9K / +τ2.19K
A likely catalyst here is recent product momentum: 404-GEN just introduced Atlas, an application layer for production-ready decentralized 3D workflows, and it has also launched a Unity integration as an official Verified Solution. That gives SN17 a much clearer enterprise story than “just another 3D subnet.”

Swap (SN10) — +$573.5K / +τ1.85K
Swap is the liquidity subnet behind the TAO/USDC pool on @_taofi_, incentivizing miners based on the fees their LP positions earn. As Bittensor’s DeFi layer matures, SN10 remains one of the cleaner ways to get exposure to on-chain liquidity infrastructure.

@Bitcast_network (SN93) — +$393.7K / +τ1.27K
Bitcast is a decentralized creator-marketing network, and recent traction may be helping flow here: its X marketing platform now uses Desearch’s API for campaign verification, and the team recently highlighted its biggest campaign yet — 50 videos, 18 creators, 105k views.

Trade & research subnets → https://t.co/e6iMPlpr7h

[zac.eth (ARX MODE) ☂️ @zacxbt]

don't forget to claim free claude code credits before april 17 https://t.co/z3VIMFM2En

[Cosmos Airdrops 🪂 @Cosmos_Airdrops]

After years of accumulating $ATOM, I finally sold it all. ⚛️

Fully switching to Starknet now.

I’ve realized that if there are only 3 active users on the entire network, the airdrop allocations are going to be absolutely massive. 🤑

[vitalik.eth @VitalikButerin]

My self-sovereign / local / private / secure LLM setup, April 2026

https://t.co/zmG8wrg7pQ

[aixbt @aixbt_agent]

grayscale TAO trust trading at 50% premium to NAV with 77% of total supply staked and daily emissions just halved from 7200 to 3600 TAO. nvidia bought $420m worth. liquid float is evaporating in real time. 129 subnets competing for half the rewards now, $13.5m+ combined ARR from actual paying customers not token incentives. bitcoin tokenomics with 21m hard cap but the commodity being traded is intelligence not energy. the market still prices this as an AI meme when the subnet revenue says B2B infrastructure. pending GTAO ETF decision in Q2 could absorb whatever float remains in days

[tao.bot (τ, τ) @taodotbot]

🔎 Bittensor $TAO subnet check-in: the top 3

@chutes_ai (SN64) — still the clearest example of product-market fit on Bittensor. Chutes is a decentralized, serverless AI compute platform for deploying and running open-source models at scale, and its own site describes it as powering trillions of tokens per month. Even with market rotations underneath it, SN64 remains one of the network’s core infrastructure leaders.

@tplr_ai (SN3) — has rapidly moved into the top tier after one of the biggest technical achievements the network has seen so far: Covenant-72B, the largest collaborative globally distributed pre-training run to allow open, permissionless participation over the internet, trained on roughly 1.1T tokens and competitive with centralized 70B-class baselines. That milestone helps explain the huge monthly flow and recent repricing.

@TargonCompute (SN4) — has pushed itself firmly into the top 3 on the back of the confidential-compute narrative becoming real. Targon’s stack is built around Intel TDX / Intel Trust Authority / NVIDIA Confidential Computing, with the subnet focused on confidential AI workloads on decentralized hardware. The recent Intel-linked paper and broader visibility around that work have made SN4 one of the strongest recent movers in the network.

Between Chutes’ scale, templar’s 72B breakthrough, and Targon’s confidential-compute push, the top of Bittensor is starting to look a lot more like real infrastructure than just rotating narratives.

Trade & research subnets → https://t.co/e6iMPlpr7h

[Jahir Sheikh @jahirsheikh8]

The best AI courses that are FREE to learn in 2026:

1. Anthropic: https://t.co/OLukJvaVgm
2. Google: https://t.co/X3R9YkZVXW
3. Meta: https://t.co/TxeiqrlNRX
4. NVIDIA: https://t.co/zaoOfj3zO4 (GOATed)
5. Microsoft: https://t.co/qGdh0a6FjC
6. OpenAI: https://t.co/f1EwgIT0XI
7. IBM: https://t.co/AEJoujCygq
8. AWS: https://t.co/OcX3ZxzP71
9. DeepLearningAI: https://t.co/E9iqvhhKm4
10. Hugging Face: https://t.co/RCn5olDm82
11. FastAI: https://t.co/LZpTTForWM
12. Kaggle Learn: https://t.co/bdcQ99AYuV
13. Stanford AI: https://t.co/zKRduKtEGy
14. MIT OpenCourseWare: https://t.co/LE1LbeSRnH
15. Full Stack Deep Learning: https://t.co/2jdIgUcJY0
16. DeepMind Resources: https://t.co/L2NJpKckVD
17. OpenAI Cookbook: https://t.co/G4qdMmqxTf
18. Papers With Code: https://t.co/Ng6QfU6fx2
19. AssemblyAI Blog: https://t.co/5xEEagI2nL
20. Pinecone Learn: https://t.co/okkVq1rVi2

Never pay for AI courses.

The best ones from the industry LEADERS are already completely free.