Allahbaksh Asadullah

Learning Computer Science, Developing Systems, Tinkering in free time, Son, Brother, Father, Husband, Uncle and Employee of Infosys (Strategic Technology Group)

[Alok Kumar @Alokkumarzz]

Instead of watching an hour of Netflix, watch this 2-hour Stanford lecture on AI careers. It will teach you more about winning in the AI race than all the AI content you’ve scrolled past this year.

[Roan @RohOnChain]

This 2 hour Stanford lecture shows exactly how Stanford trains it's engineers to build AI systems. It's more practical than every Claude tutorial & prompting threads you've seen.

Bookmark & give it 2 hours, no matter what. It'll be the most productive thing you do this weekend.

[Nainsi Dwivedi @NainsiDwiv50980]

Most people think using Claude Code is about writing better prompts.

It’s not.

The real unlock is structuring your repository so Claude can think like an engineer.

If your repo is messy, Claude behaves like a chatbot.

If your repo is structured, Claude behaves like a developer living inside your codebase.

Your project only needs 4 things:

• the why → what the system does
• the map → where things live
• the rules → what’s allowed / forbidden
• the workflows → how work gets done

I call this:

The Anatomy of a Claude Code Project 👇

━━━━━━━━━━━━━━━

1️⃣ CLAUDE.md = Repo Memory (Keep it Short)

This file is the north star for Claude.

Not a massive document.

Just three things:

• Purpose → why the system exists
• Repo map → how the project is structured
• Rules + commands → how Claude should operate

If CLAUDE.md becomes too long, the model starts missing critical signals.

Clarity beats size.

━━━━━━━━━━━━━━━

2️⃣ .claude/skills/ = Reusable Expert Modes

Stop repeating instructions in prompts.

Turn common workflows into reusable skills.

Examples:

• code review checklist
• refactoring playbook
• debugging workflow
• release procedures

Now Claude can switch into specialized modes instantly.

Result:

More consistent outputs across sessions and teammates.

━━━━━━━━━━━━━━━

3️⃣ .claude/hooks/ = Guardrails

Models forget.

Hooks don’t.

Use hooks for things that must always happen automatically.

Examples:

• run formatters after edits
• trigger tests after core changes
• block sensitive directories (auth, billing, migrations)

Hooks turn AI workflows into reliable engineering systems.

━━━━━━━━━━━━━━━

4️⃣ docs/ = Progressive Context

Don’t overload prompts with information.

Instead, let Claude navigate your documentation.

Examples:

• architecture overview
• ADRs (engineering decisions)
• operational runbooks

Claude doesn’t need everything in memory.

It just needs to know where truth lives.

━━━━━━━━━━━━━━━

5️⃣ Local CLAUDE.md for Critical Modules

Some areas of your system have hidden complexity.

Add local context files there.

Example:

src/auth/CLAUDE.md
src/persistence/CLAUDE.md
infra/CLAUDE.md

Now Claude understands the danger zones exactly when it works in them.

This dramatically reduces mistakes.

━━━━━━━━━━━━━━━

Here’s the shift most people miss:

Prompting is temporary.

Structure is permanent.

Once your repository is designed for AI:

Claude stops acting like a chatbot...

…and starts behaving like a project-native engineer. 🚀

[Leonard Rodman @RodmanAi]

Everything you need to know for coding interviews in one roadmap.

Coding Interviews
│
├── 1. Data Structures
│ ├── Arrays
│ ├── Strings
│ ├── Hashmaps
│ ├── Linked Lists
│ │ ├── Singly
│ │ └── Doubly
│ ├── Stacks
│ ├── Queues
│ ├── Deques
│ ├── Heaps / Priority Queues
│ ├── Trees
│ │ ├── Binary Tree
│ │ ├── Binary Search Tree
│ │ ├── AVL Tree
│ │ └── Red-Black Tree
│ ├── Tries
│ ├── Segment Trees
│ └── Fenwick Trees
│
├── 2. Core Problem Patterns
│ ├── Two Pointers
│ ├── Sliding Window
│ ├── Binary Search
│ ├── Recursion
│ ├── Backtracking
│ ├── Greedy Algorithms
│ ├── Divide & Conquer
│ └── Sorting
│ ├── Merge Sort
│ ├── Quick Sort
│ └── Heap Sort
│
├── 3. Dynamic Programming
│ ├── Memoization
│ └── Tabulation
│
├── 4. Graph Algorithms
│ ├── BFS
│ ├── DFS
│ ├── Topological Sort
│ ├── Dijkstra
│ ├── Bellman-Ford
│ ├── Floyd-Warshall
│ ├── Kruskal
│ ├── Prim
│ └── Union Find
│
├── 5. Advanced Techniques
│ ├── Bit Manipulation
│ ├── XOR Tricks
│ ├── Prefix Sums
│ ├── Difference Arrays
│ ├── Kadane’s Algorithm
│ ├── KMP
│ ├── Rabin-Karp
│ ├── Z Algorithm
│ └── Manacher’s Algorithm
│
├── 6. Object-Oriented Design
│ ├── SOLID Principles
│ └── Design Patterns
│ ├── Singleton
│ ├── Factory
│ ├── Builder
│ ├── Adapter
│ ├── Observer
│ ├── Strategy
│ └── Visitor
│
├── 7. System Design
│ ├── CAP Theorem
│ ├── Load Balancing
│ ├── Caching (LRU / LFU)
│ ├── SQL vs NoSQL
│ ├── Sharding
│ ├── Replication
│ ├── Message Queues
│ │ ├── Kafka
│ │ └── RabbitMQ
│ ├── Microservices
│ └── API Gateway
│
├── 8. Design Problems
│ ├── URL Shortener
│ ├── Twitter
│ ├── WhatsApp
│ ├── Netflix
│ ├── Google Drive
│ ├── Payment System
│ ├── Notification System
│ └── Live Streaming Platform
│
└── 9. Interview Survival Skills
├── Thinking Out Loud
├── Recovering from Bugs
├── Explaining Tradeoffs
└── Staying Calm Under Pressure

Save this before your next interview.

[Anant Ladha, CFA CA CFP LL.B. @anantladha25]

❤️❤️

[Brandon Ballinger @bballinger]

The government wants AI doctors.

In 60 days, four new initiatives have launched:
- ARPA-H ADVOCATE: funding an autonomous AI cardiologist.
- CMS ACCESS: pays for outcomes, not visits, with reimbursement set that you have to use software to scale.
- FDA TEMPO: lets AI devices deploy before full clearance
- Utah: AI can prescribe medication (refills at first).

AI is coming to care delivery.

[Stanislav Kozlovski @kozlovski]

Apache Kafka 4.2.0 is about to drop any minute now! 🔥

A heavy release, it introduces close to 32,000 new lines of Java. What comes with all that code?

Here's everything you should know about it:

1/8 🧵

[Md Ismail Šojal 🕷️ @0x0SojalSec]

JSON data visualization tool.⚔️

- https://t.co/mVQDx6JSqj

#infosec #cybersec #bugbountytips

[Allahbaksh Asadullah @allahbaksh]

@ProjNanda Looking forward to register and start a chaper at Hubballi. Can you please help us with the same.

[Namanyay @NamanyayG]

I'm giving Claude Code for FREE

(yes the world's best AI -- at no cost at all)

My mission is to get the next billion people to write great software, but so many are constrained by costs.

We solved this by combining AI with ads (only contextual and relevant ads, no spam).

In return, you get the best AI models without needing a credit card.

For the next batch, I will choose 500 people randomly who have done the following steps:

1. Comment "GIVE ME SANTA CLAUDE" below
2. Like and retweet this
3. Follow @NamanyayG and @supermemory
4. Sign up on the link in the next tweet

Watch this quick video to understand how it works!

[Chainlink @chainlink]

We’re excited to announce that @UBS has successfully completed the world’s first in-production, end-to-end tokenized fund workflow leveraging the Chainlink Digital Transfer Agent (DTA) technical standard.

https://t.co/vY8LIKEPlI

UBS—one of the world’s largest private banks with over $6 trillion in AUM—worked with its in-house tokenization unit UBS Tokenize and DigiFT to showcase a live, in-production tokenized fund transaction that leverages the Chainlink DTA technical standard to complete the first-ever subscription and redemption request of a tokenized fund.

This development marks a major achievement that builds upon prior work between UBS and Chainlink within the Monetary Authority of Singapore’s Project Guardian initiative and proves how fund operations can be seamlessly automated onchain for increased efficiency and utility gains. In this live transaction, DigiFT functioned as the onchain fund distributor and leveraged the DTA standard to successfully request and process a subscription and redemption order.

This new end-to-end tokenized fund workflow can cover every stage of the fund lifecycle, including order taking, execution, settlement, and data synchronization across all onchain and offchain systems.

The DTA technical standard leverages key Chainlink platform capabilities, including:

• Chainlink Runtime Environment (CRE) for orchestration across onchain environments and existing in-house systems used by financial institutions.

• Cross-Chain Interoperability Protocol (CCIP) for interoperability across any public or private chain.

• Automated Compliance Engine (ACE) for programmable compliance.

• NAVLink for robust pricing inputs required for fund subscriptions and redemptions.

[Sundar Pichai @sundarpichai]

Our TPUs are headed to space! 

Inspired by our history of moonshots, from quantum computing to autonomous driving, Project Suncatcher is exploring how we could one day build scalable ML compute systems in space, harnessing more of the sun’s power (which emits more power than 100 trillion times humanity’s total electricity production).

Like any moonshot, it’s going to require us to solve a lot of complex engineering challenges. Early research shows our Trillium-generation TPUs (our tensor processing units, purpose-built for AI) survived without damage when tested in a particle accelerator to simulate low-earth orbit levels of radiation. However, significant challenges still remain like thermal management and on-orbit system reliability. 

More testing and breakthroughs will be needed as we count down to launch two prototype satellites with @planet by early 2027, our next milestone of many. Excited for us to be a part of all the innovation happening in (this) space!

[Allahbaksh Asadullah @allahbaksh]

@passportsevamea Sir for wife the address proof can be husband passport. This is part of the FAQ and the form. The AOP of Hubli Dharwad PSK it can't be. Can you please help in this (either update the document FAQ and drop down or ask AOP to consider it )

[Allahbaksh Asadullah @allahbaksh]

@redBus_in PNR No 224285805. I have not heard anything on the refund on change of bus from Volvo Multi Axle to normal Eicher bus. #CustomerExperience #CustomerServiceFail #redbus

[Allahbaksh Asadullah @allahbaksh]

@passportsevamea What to do if Hubballi PSK AOP does not accept husband passport as the proof of address. This was harrassing experience as it is mentioned as valid document for wife address.

[Allahbaksh Asadullah @allahbaksh]

@redBus_in The #customerservice of yours has gone down drastically. In fact the way it is run now it is one of the #worst service.

[Allahbaksh Asadullah @allahbaksh]

@passportsevamea Please note the Mobile OTP is not working. The OTP is not sent across. This is for renewal of passport to capture populate all the provided details.

[🚨 AI News | TestingCatalog @testingcatalog]

BREAKING 🚨: NotebookLM is working on Slides generation! Soon, it will be able to generate a presentation from your sources.

It will be huge for Enterprise and Business customers!

Tons of SAAS apps build the same thing 👀

[Rohan Paul @rohanpaul_ai]

Massive. 🤯

Alibaba introduced a new GPU pooling system Aegaeon that makes AI model serving much more efficient.

Claims an 82% cut in Nvidia GPU use for serving LLMs by pooling compute across models.

In a 3+ month beta on Alibaba Cloud’s marketplace, H20 GPUs dropped from 1,192 to 213 while serving dozens of models up to 72B parameters.

The regular Cloud model hubs skew toward a few hot models, so many GPUs sit idle for cold models, and Alibaba measured 17.7% of GPUs handling only 1.35% of requests.

Aegaeon addresses this with token-level auto-scaling, which lets a GPU switch between models during generation instead of waiting for a full response to finish.

By slicing work at token boundaries and scheduling small bursts quickly, the system keeps memory warm and compute busy with little waste.

With Aegaeon, a single GPU supports up to 7 models versus 2 to 3 in other pooling systems, and switching latency drops by 97%.

Cold models load weights just in time when a request lands, then borrow a brief slice of compute without locking an entire GPU.

Hot models keep priority, so heavy traffic stays smooth while sporadic models borrow capacity in short bursts.

The wins apply to inference, not training, because generation happens token by token and fits fine-grained scheduling.

The timing suits China’s chip limits, where H20 targets inference workloads and domestic GPUs are ramping, so fewer chips can cover more traffic.

If Aegaeon generalizes, operators can lower cost per token, raise fleet utilization, and delay new GPU purchases without hurting latency for popular models.

Tradeoffs still exist, like uneven memory needs across models, long sequences that reduce preemption points, and scheduler overhead during traffic spikes.

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scmp. com/business/article/3329450/alibaba-cloud-claims-slash-nvidia-gpu-use-82-new-pooling-system

[Allahbaksh Asadullah @allahbaksh]

@code We need this at Hubballi, India. It is around 400Km from Bangalore and well connected from Bangalore, Mumbai, Hyderabad.