Follows and tweets on AI - AI Transformation- Product Management* Public Cloud* Cyber Security*
Chennai, India
Joined May 2009
- Tweets1.2K
- Following2.3K
- Followers191
- Likes1.7K
Nirmal retweeted
I have taken so many pictures of the sign, especially in winter because it's usually dark when I leave the office.
Camera: RICOH GR IIIx
Focal Length: 26.0 mm
Aperture: f/2.8
Exposure: 1/100
ISO: 400
photos.deepaksingh.net/World…
#dailyphoto
Nirmal retweeted
I just created a playbook to help you learn AI engineering.
It gives you:
• Core concepts behind modern AI systems.
• Condensed notes to understand AI engineering stack.
• Must know techniques to build, deploy, debug & scale AI apps.
(24 hours only!)
To get it for FREE:
1. Like, Retweet & Follow @systemdesignone
2. Reply "Playbook"
Then I'll DM you the details.
Nirmal retweeted
some thoughts on working with ai models
• context as infra
• taste as config
• verification for autonomy
• scaling via delegation
• closing the loop
eugeneyan.com/writing/workin…
Nirmal retweeted
The Harness is a Context Manager on Behalf of the Model
What happens when the context window fills up and who decides? This decision is external to the model - The Harness designer must have some opinion here! Decisions like this are crucial in turning a model into a great product for end users.
The context window is a sacred boundary beyond which all model computation actually happens. Context engineering is important because designing what gets passed over this boundary is the main determinant of agent performance. Harness design is how you decide how this boundary gets managed.
Our create_agent primitive in LangChain exposes the one of the simplest Harnesses for builders to extend, a ReAct loop with support for tools, middleware (hooks), and model choice
It’s a great place to start in agent building because it forces you to think through and contend with all of the design details that transform a simple agent loop into a purpose-built agent for your tasks
The first time you hit the context boundary in a simple agent loop, the API will just error out and your agent run will end. The API contract only supports a max number of tokens.
A harness helps you get in line with the API contract by managing context via strategies like truncation, compaction, offloading, and targeted context eviction
This is just one decision to think about in Harness Design, many more come up as you build such as agent specialization via Subagents, Tool design, Skill design, and more. Each of these are important in extending a model to make it into a useful product for users.
create_agent is a great level of abstraction to start building agents. Builders can go up a level to deepagents for a more out of the box agent experience and even further to Fleet as a more out of the box product experience. Or they can go down to the runtime execution level to LangGraph like @caspar_br had talked about
starting from a simple harness to build a great agent helps you learn fundamentals of how models work + good design patterns that turn them into great agents and products
An interactive, real-time visualization of how LLM context windows fill up — including MCP server overhead, KV cache state, context rot, and token distribution across message roles.
nirmal84.github.io/llm-conte…
Nirmal retweeted
📣 Shipping software with Codex without touching code.
Here’s how a small team steering Codex opened and merged 1,500 pull requests to deliver a product used by hundreds of internal users with zero manual coding.
openai.com/index/harness-eng…
Nirmal retweeted
New Google paper says LLM inference is limited by memory and networking, and it proposes 4 hardware shifts.
Inference is separated into Prefill, which processes input in parallel, and Decode, which outputs 1 token (a small text chunk) at a time while constantly reading a Key Value cache, a running memory of past text.
LLM serving speed and cost problems come from the Decode phase, where the system keeps fetching past context from memory and talking across chips, not from a lack of raw compute.
The paper argues today’s data center accelerators were shaped by training needs, so they often look fast on paper but waste time during Decode because memory bandwidth, memory capacity, and interconnect latency become the real bottlenecks.
The authors review today’s hardware and show a mismatch, high compute helps less when Decode gets stuck on memory bandwidth, memory capacity, and network latency.
High Bandwidth Flash stacks flash to aim for about 10X more capacity for model weights, the learned numbers that stay fixed during inference, or for slow changing context, and processing near memory plus 3D stacking move compute closer to memory so less energy and time are wasted moving data around.
For fast user responses, the paper also pushes low latency interconnects, meaning fewer network hops and smarter switches, so the same LLM can run on a smaller cluster that costs less and fails less.
----
Paper Link – arxiv. org/abs/2601.05047
Paper Title: "Challenges and Research Directions for LLM Inference Hardware"
Nirmal retweeted
📰🚨 AWS DevOps Agent helps you accelerate incident response and improve system reliability (preview)
#AWSDevOpsAgent #AIinDevOps #IncidentManagement #CloudComputing #Automation
ift.tt/MdWE5aT
Nirmal retweeted
Really enjoyed Matt’s keynote at #AWSreInvent today. So much innovation happening in @awscloud, and you could see it with the array of launches he unveiled.
So many parts of the keynote worth watching, but will point to a few:
1/ Excited about the availability of Trainium3. Trainium2 has substantial traction, is a multi-billion-dollar revenue run rate business, has 1M+ chips in production, and 100K+ companies using it as the majority of Bedrock usage today. Trainium2 has price-performance advantages over other GPU options that are compelling, and Trainium3 will deliver at least 4.4x more compute performance, 4x greater energy efficiency, and almost 4x more memory bandwidth than Trainium2: youtu.be/q3Sb9PemsSo?si=0rVn…
2/ Worth double clicking on AgentCore, which has changed the security and scalability of deploying agents into production. AgentCore is a set of flexible building blocks that can be used in any combination developers want, and AWS added two more in Policy and Evaluations. AgentCore has a lot of momentum: youtu.be/q3Sb9PemsSo?si=0Qj1…
3/ Nova Forge is a game-changer for companies wanting to customize a frontier model with their own proprietary data. Like equipping a young person with a better knowledge foundation to keep learning, LLMs are better able to solve problems and improve if they’re trained early on with companies’ differentiated data. To do so, companies need earlier versions of the frontier model and ability to mix their own data with the model’s data. This is what Forge provides and this “open training” allows companies to develop Novellas that are their own, optimized versions of Nova they can use for their AI apps and agents. Customers have been itching for this sort of capability, and Forge is a uniquely compelling approach: youtu.be/q3Sb9PemsSo?si=S0CJ…
4/ Agents will become the primary way companies get value from AI. We have built some compelling agents for our customers in Kiro (for coding), Quick (for knowledge workers to leverage their own data, analytics, and routines), Transform (to migrate from one software source to another), and Connect (call center agents). But there are tasks customers want agents to solve more autonomously and over longer durations, and new AWS frontier agents—Kiro autonomous agent, AWS DevOps Agent, and AWS Security Agent—are exciting: youtu.be/q3Sb9PemsSo?si=R95D…
5/ Finally, I enjoyed Matt’s ending 25 launches in 10 mins, both because it was action-packed and represented so much useful delivery. The reason so many people cheered these launches is because even though they’re less sexy, they’re the meat and potatoes core infrastructure needs customers have—and with so much of the total cloud infrastructure running on top of AWS, these launches will make a lot of people’s lives easier and better every day: youtu.be/q3Sb9PemsSo?si=Gusf…
Enjoy (and just day 1 of announcements today for AWS :-)!
As a customer or business user I care about the features and want a working software to accomplish my tasks , I don’t care about complexity of the underlying systems, this is where vibe coding so powerful. Garry is right in the sense it’s easy to vibe code a fully functional SaaS in a weekend companies like Zoho should reinvent from per seat to outcomes driven model. @svembu @moneycontrolcom
Zoho’s business would be first to be competed away by people building their own custom software built by people using @Replit @emergentlabs and @Taskade
Why pay $30/seat/month for over bundled SaaS when soon even nontech ops ppl can vibe-code a custom solution in a weekend?
Nirmal retweeted
Zoho’s business would be first to be competed away by people building their own custom software built by people using @Replit @emergentlabs and @Taskade
Why pay $30/seat/month for over bundled SaaS when soon even nontech ops ppl can vibe-code a custom solution in a weekend?
Nirmal retweeted
Just created a complete analysis of AI infrastructure opportunities covering chip manufacturers, power companies and system integrators.
I shared this analysis with my 20,000+ students.
For 24 hours, it's yours for FREE.
Like, RT & Comment "AI" and I'll DM it to you.
@BYD_AsiaPacific I purchased my BYD Sealion 6 from Melbourne Australia in March 2025 - since then I have had air con problems in my car for which I had visited thrice in the last 6 months and they are not able to fix this , every time I go someone checks and works for a day and fills the gas - after couple of weeks the problems repeats. no one is able
To own and fix it properly.
Nirmal retweeted
Just leaked 110+ plug-and-play n8n templates that multiple industries are paying me $10K+ for.
Your weekend project: Pick any industry, copy-paste a workflow, start printing money.
The vault includes:
- Real estate lead machines (sold to 2 companies)
- Automotive inventory bots
- YouTube-to-TikTok content farms
- E-commerce customer journey automations
- SDR agents that close without you
- Healthcare patient follow-ups
- Legal document processors
- Social media engagement bots
From study agents (because fuck school) to manufacturing systems - every industry that prints money is covered.
The difference: These aren't broken templates with outdated nodes. They're battle-tested, profitable, and actually work in 2025.
Half sold for serious cash. Half are experiments that became goldmines. All are ready to deploy.
Follow, RT + Comment "vault" and I'll drop the entire collection before my accountant kills me for giving away the farm.
(Sorry to every consultant charging $10K for basic automations)
Nirmal retweeted
How Docker Works Internally.
Explained Simply 👇
47K+ read my TechOps Examples newsletter: techopsexamples.com/subscrib…
What do we cover:
DevOps, Cloud, Kubernetes, IaC, GitOps, MLOps
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