Sami Arja

PhD Student in Neuromorphic Remote Sensing and Surveillance at ICNS

[Ralph Sueppel @macro_synergy]

"The Trades That Work Because Nobody Wants to Do Them"
https://t.co/WgMmI5EXK6

[柴郡🔔｜Crypto+AI Plus @0xCheshire]

看见一个很离谱的论文……

如果你每天在收盘时买入美光科技，在第二天开盘时立刻卖出，几十年来会获得高达 138,330,342% 的收益。

但如果每天开盘买入，当天收盘时卖出，你的投资最终会几乎全部亏损-99.92%。

作者表示，这一切都是既得利益者的阴谋，所有市场都一样。

是的，包括咱们大 A。

[Stat Arb @quant_arb]

This is a great read for active HFT researchers and breaks down research steps I use regularly in my own processes

[Roan @RohOnChain]

Jane Street pays $650,000 a year for quants. MIT wrote the exact bible to get there & released it for free.

51 pages. Zero to quant. Probability, stats, market making, real interview questions from Jane Street, Citadel, Two Sigma & more. Bookmark, before someone takes it down.

[Christina Qi @christinaqi]

HFT vs. AI: Here's the estimated trading revenues for 5 firms generally considered the fastest in major US & European markets... alongside 4 firms considered the best in ML-driven trading.
* To be clear, the ML firms aren't slow. They have FPGA engineers, lease microwave networks, etc.
* But the data clearly shows that the edge in algo trading has shifted from latency to ML.

So like... why? I gave a talk at NVIDIA GTC about this, time to unravel it here.

[Ryota Kanai @kanair]

Great to see this on X. This is something I created during my PhD time 20 years ago.

[sui ☄️ @birdabo]

SOMEONE MADE A DIGITAL WHIP TO MAKE CLAUDE WORK FASTER 💀

[Sagar @code_sagar]

There goes 80% architect’s daily grind

[Josh tried coding @joshtriedcoding]

quick claude code tip

you can stop claude from being listed as a co-author on commits by changing this line 👀

[HyperSwap @HyperSwapX]

Tap trading on $HYPE is coming

Mainnet launch soon

[Tech with Mak @techNmak]

Someone finally documented how to actually use Claude Code.

22K+ stars. claude-code-best-practice.

Direct from Boris Cherny and team:

→ Always use plan mode, give Claude a way to verify
→ Ask Claude to interview you using AskUserQuestion tool
→ Use Git Worktrees for parallel development
→ /loop - schedule recurring tasks for up to 3 days
→ Code Review - fresh context windows catch bugs the original agent missed
→ /btw - side chain conversations while Claude works
→ Make phase-wise gated plans with tests for each phase
→ Use cross-model (Claude Code + Codex) to review your plan
→ CLAUDE[.]md should target under 200 lines per file
→ Use commands for workflows instead of sub-agents
→ Have feature-specific sub-agents with skills instead of general QA or backend engineer
→ Vanilla Claude Code is better than complex workflows for smaller tasks
→ Take screenshots and share with Claude when stuck
→ Use MCP to let Claude see Chrome console logs
→ Ask Claude to run terminal as background task for better debugging
→ Use cross-model for QA - e.g. Codex for plan and implementation review

The community workflows included:
→ Cross-Model (Claude Code + Codex) Workflow
→ RPI (Research Plan Implement)
→ Ralph Wiggum Loop for autonomous tasks
→ Github Speckit (74K stars)
→ obra/superpowers (72K stars)
→ OpenSpec OPSX (28K stars)

The billion-dollar questions it addresses:
→ What should you put inside CLAUDE[.]md?
→ When should you use command vs agent vs skill?
→ Why does Claude ignore CLAUDE[.]md instructions?
→ Can we convert a codebase into specs and regenerate code from those specs alone?

The daily habits:
→ Update Claude Code daily
→ Start your day by reading the changelog
→ Follow r/ClaudeAI, r/ClaudeCode on Reddit

Repost it. Bookmark it.

[Tom Dörr @tom_doerr]

Interactive trading strategy trainer

https://t.co/Voj4PF1Gor

[Suryansh Tiwari @Suryanshti777]

🚨Breaking: The guy who created Claude Code (@bcherny) just revealed how his team actually trains their AI.

One file: CLAUDE.md

You place it at the root of your project.

Inside it:

past mistakes

conventions

rules

Claude reads it every session.

The result?

The agent improves over time without you touching the code.

Every bug that gets fixed becomes a permanent rule.

Boris Cherny uses this internally at Anthropic every day.

Here’s the template he shared — ready to copy, paste, and adapt.

CLAUDE.md Template

1. Plan Mode Default

Enter plan mode for any non-trivial task (3+ steps or architectural decisions)

If something goes wrong, STOP and re-plan immediately — don’t keep pushing

Use plan mode for verification steps, not just building

Write detailed specs upfront to reduce ambiguity

2. Subagent Strategy

Use subagents frequently to keep the main context window clean

Offload research, exploration, and parallel analysis to subagents

For complex problems, throw more compute via subagents

Assign one task per subagent for focused execution

3. Self-Improvement Loop

After any correction from the user, update tasks/lessons.md with the pattern

Write rules for yourself to prevent repeating the same mistake

Ruthlessly iterate on these lessons until the mistake rate drops

Review lessons at the start of each session

4. Verification Before Done

Never mark a task complete without proving it works

Diff behavior between main and your changes when relevant

Ask yourself: “Would a staff engineer approve this?”

Run tests, check logs, and demonstrate correctness

5. Demand Elegance (Balanced)

For non-trivial changes, ask: “Is there a more elegant solution?”

If a fix feels hacky, ask:
“Knowing everything I know now, implement the elegant solution.”

Skip this for simple fixes — don’t over-engineer

Challenge your own work before presenting it

6. Autonomous Bug Fixing

When given a bug report: just fix it

Use logs, errors, and failing tests to diagnose

Require zero context switching from the user

Fix failing CI tests automatically

Task Management

1. Plan First – Write the plan in tasks/todo.md with checkable items

2. Verify Plan – Confirm the plan before implementation

3. Track Progress – Mark items complete as you go

4. Explain Changes – Provide a high-level summary at each step

5. Document Results – Add a review section to tasks/todo.md

6. Capture Lessons – Update tasks/lessons.md after corrections

Core Principles

Simplicity First
Make every change as simple as possible and minimize code impact.

No Laziness
Find root causes. Avoid temporary fixes. Maintain senior-level engineering standards.

[JDK Analysis 🇪🇺 @The_JDK99]

Learn how to read OrderFlow. You can thank me later 👀

I put together a detailed introduction that covers everything you need to understand my posts.

There will be a Part 2, where we will go even deeper with real setups, and execution. Stay tuned! $BTC
https://t.co/hfNaTMWUJI

[Om Patel @om_patel5]

software engineers right now:

[Mayank Pratap Singh @Mayank_022]

𝐕𝐢𝐬𝐮𝐚𝐥 𝐛𝐥𝐨𝐠 on Vision Transformers is live.
https://t.co/N09njkKTXW

Learn how ViT works from the ground up, and fine-tune one on a real classification dataset.

CNNs process images through small sliding filters. Each filter only sees a tiny local region, and the model has to stack many layers before distant parts of an image can even talk to each other.

Vision Transformers threw that whole approach out.

ViT chops an image into patches, treats each patch like a token, and runs self-attention across the full sequence.
Every patch can attend to every other patch from the very first layer. No stacking required.

That global view from layer one is what made ViT surpass CNNs on large-scale benchmarks.

𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐥𝐨𝐠 𝐜𝐨𝐯𝐞𝐫𝐬:

- Introduction to Vision Transformers and comparison with CNNs
- Adapting transformers to images: patch embeddings and flattening
- Positional encodings in Vision Transformers
- Encoder-only structure for classification
- Benefits and drawbacks of ViT
- Real-world applications of Vision Transformers
- Hands-on: fine-tuning ViT for image classification

The Image below shows

Self-attention connects every pixel to every other pixel at once. Convolution only sees a small local window. That's why ViT captures things CNNs miss, like the optical illusion painting where distant patches form a hidden face.

The architecture is simple. Split image into patches, flatten them into embeddings (like words in a sentence), run them through a Transformer encoder, and the class token collects info from all patches for the final prediction. Patch in, class out.

Inside attention: each patch (query) compares itself to all other patches (keys), softmax gives attention weights, and the weighted sum of values produces a new representation aware of the full image, visualizes what the CLS token actually attends to through attention heatmaps.

The second half of the blog is hands-on code. I fine-tuned ViT-Base from google (86M params) on the Oxford-IIIT Pet dataset, 37 breeds, ~7,400 images.

𝐁𝐥𝐨𝐠 𝐋𝐢𝐧𝐤
https://t.co/N09njkKTXW

𝐒𝐨𝐦𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬

Dr @sreedathpanat Videos on ViT
ViT paper dissection
https://t.co/sg3JRvcgNG

Build ViT from Scratch
https://t.co/cnHzEeefDA

Original Paper
https://t.co/QiwrlDRQOc

Next up: demystifying Low-Rank Adaptation (LoRA) in PEFT!

Follow me @Mayank_022 along for more deep learning insights, cool fine-tuning projects, and updates from the upcoming blog posts.

[Tom Dörr @tom_doerr]

Deep time series analysis library

https://t.co/a8nrzauDJK

[GitHub Projects Community @GithubProjects]

If you can’t explain it simply, you don’t understand it.

This repo fixes that.

Turn any concept into clear, structured explanations that actually make sense.

• Simplifies complex ideas
• Builds real understanding
• Works for code + concep

[exitpump @exitpumpBTC]

Read

[GitHub Projects Community @GithubProjects]

This isn’t a chatbot.

MiroFish Offline spins up a simulated world of agents that interact and evolve to explore outcomes.

Run it locally. Watch the system unfold.

• Swarm intelligence
• Parallel reasoning
• Offline execution