vivek kolasani

Techie

[CNCF @CloudNativeFdn]

Move beyond one model per GPU.

Join @vivek_kolasani of @nvidia at #KubeCon + #CloudNativeCon to see agents, constraint solvers and Kubernetes team up for smarter workload placement.

📅 November 9-12
📍 Salt Lake City

Register: https://t.co/dSmoY5pqlF

[GREG ISENBERG @gregisenberg]

I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.

My observations:

1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.

2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.

3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.

4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.

5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.

6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.

They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.

7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.

8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.

Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.

9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.

10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!

11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.

12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.

13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.

14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.

15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.

16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.

17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?

18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.

19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.

20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.

21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.

Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.

Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.

Happy to be home and locked in.

Until next time!

[a16z @a16z]

Introducing Cosign.

https://t.co/xf9tfYqVwn

[Shubh Jain @shubh19]

Deloitte Most Asked JAVA DSA Questions in Interview

LeetCode Problem No:
1 - Two Sum
2 - Add Two Numbers
9 - Palindrome Number
19 - Remove Nth Node From End
20 - Valid Parentheses
21 - Merge Two sorted lists
22 - Generate Parentheses
23 - Merge k Sorted Lists
24 - Swap Nodes in Pairs
25 - Reverse Nodes in k-Group
26 - Remove Duplicates from Sorted Array
27 - Remove Element
28 - Implement strStr()
33 - Search in Rotated Sorted Array
35 - Search Insert Position
36 - Valid Sudoku
42 - Trapping Rain Water
46 - Permutation (all permutations)
47 - Permutations II (with duplicates)
48 - Rotate Image
50 - Pow(x, n)
53 - Maximum Subarray
54 - Spiral Matrix
66 - Plus One
70 - Climbing Stairs
73 - Set Matrix Zeroes
74 - Search a 2D matrix
76 - Minimum Window Substring
77 - Combinations (n choose k)
79 - Word Search
84 - Largest Rectangle in Histogram
88 - Merge Sorted Array
103 - Binary Tree Zigzag Level Order
104 - Maximum Depth of Binary Tree
105 - Construct Binary Tree from Preorder and Inorder
106 - Construct Binary Tree from Inorder and Postorder
108 - Convert Sorted Array to BST
112 - Path Sum
113 - Path Sum II
121 - Best time to buy and sell stock
124 - Binary Tree Maximum Path Sum
125 - Valid Palindrome
127 - Word Ladder (Length)
136 - Single Number
142 - Linked List Cycle II (find start)
150 - Evaluate Reverse Polish Notation
152 - Maximum Product Subarray
155 - Min Stack
160 - Intersection of Two Linked Lists
162 - Find Peak Element
173 - Binary Search Tree Iterator
189 - Rotate Array
198 - House Robber
206 - Reverse Linked List
210 - Topological Sort / Course Schedule (II)
213 - House Robber II
215 - Kth Largest Element in an Array
217 - Contains Duplicate
225 - Implement Stack using Queues
231 - Power of Two
232 - Implement Queue using Stacks
235 - Lowest Common Ancestor of BST
236 - Lowest Common Ancestor of Binary Tree
268 - Missing Number
283 - Move Zeroes
297 - Serialize and Deserialize Binary Tree
344 - Reverse String
347 - Top K Frequent Elements
349 - Intersection of Two Arrays
509 - Fibonacci Number
973 - K Closest Points to Origin
1464 - Maximum Product of Two Elements

[Suraj Sharma @suraj_sharma14]

If I had 6 months to become an AI Evals & Reliability Engineer.

I'd do this.

Stage 1: Python and Testing Foundations
Python, pytest, Git, CI/CD basics, JSONL data handling, pandas.

Stage 2: LLM Behavior Fundamentals
Tokens, sampling, temperature, reasoning effort, why outputs are non-deterministic.

Stage 3: Evaluation Fundamentals
Golden datasets, exact vs fuzzy matching, LLM-as-a-judge, rubric design, judge bias control.

Stage 4: RAG Evaluation
Hit rate, MRR, NDCG, RAGAS faithfulness and relevance, citation grounding checks.

Stage 5: Agent and Trajectory Evaluation
Tool-call correctness, step-level grading, counterfactual runs, trajectory datasets.

Stage 6: Eval Frameworks and Pipelines
DeepEval, promptfoo, RAGAS, OpenAI Evals; wiring evals into CI regression gates.

Stage 7: Observability and Tracing
Langfuse, LangSmith, Arize Phoenix, OpenTelemetry spans, production sampling, failure mining.

Stage 8: Production Monitoring and Drift
Hallucination spike alerts, quality drift detection, shadow traffic, canary evals.

Stage 9: Reliability Patterns
Circuit breakers, fallback chains, retries with backoff, idempotency, graceful degradation.

Stage 10: Durable Execution and Chaos
Temporal checkpoints, LLM load testing, chaos experiments, infinite loop detection.

Stage 11: Cost and FinOps Guardrails
Cost per request and per tenant, budget alerting, kill-switches, caching ROI measurement.

Stage 12: Incident Response and Portfolio
SLOs for AI systems, runbooks, post-mortems, public reliability writeups, open-source eval tooling.

Most people stay stuck watching tutorials.

Builders get hired.

(Bookmark it)

[Satya Nadella @satyanadella]

Any pursuit of superintelligence has to be grounded in the core principle that if the AI we build is not helping humanity and under human control, it's not worth pursuing.

We also need to accelerate and spread the benefits of AI, such that they are diffused broadly across countries, communities, and companies. This requires a frontier ecosystem in which both closed and open-source models can thrive.

And for firms, it’s imperative that they retain full control over their unique and tacit knowledge. Every organization should be able to build its own continuous learning loop/hill climbing machine, without becoming dependent on any one model provider, and have the ability to embed its own knowledge into models and weights they control.

So, in this context, we welcome the research, focus, and deliberate pacing needed to get alignment right as the design goal. We also welcome ideas like "embedded evaluators" and the broader efforts to develop the mechanisms to make this more than just talk.

The key is that this cannot be controlled by a handful of entities, but must have broad representation across the ecosystem, countries, and fields, including academia.

This is the approach we are taking: broad access and choice at every layer of the AI stack; enterprise control of learning loops and models; and the “Code of Conduct” that underlies our own first party MAI models that we’ll publish tomorrow for public consultation.

[Suraj Sharma @suraj_sharma14]

As an AI Infrastructure Engineer, you must build these projects.

Systems that prove you can serve, scale & keep GPUs honest.

1.) Self-Hosted Inference Cluster
Build: Multi-node GPU cluster serving an open model with vLLM or SGLang, health checks, and a public latency report.
Why: Talk is cheap. A live cluster is the portfolio.

2.) Cost-Per-Token Dashboard
Build: TTFT, ITL, GPU util (DCGM) and $ per 1M tokens by model, tenant and route.
Why: Infra without FinOps is just expensive uptime.

3.) Queue-Based GPU Autoscaler
Build: KEDA (or equivalent) scaling on queue depth, with cold-start mitigation and spot fallback.
Why: Idle GPUs kill startups. Slow scale kills users.

4.) Continuous Batching Load Test
Build: Traffic generator that stresses continuous batching, KV cache limits and saturation points.
Why: You don’t know your serving stack until it breaks under load.

5.) Model Weight Delivery System
Build: Safetensors registry + sharded weights + CDN/lazy loading for fast node bring-up.
Why: Cold starts are often a storage problem pretending to be a compute problem.

6.) Multi-Model AI Gateway
Build: Routing, retries, fallbacks, rate limits and per-tenant token budgets across 2–3 serving backends.
Why: Production isn’t one endpoint. It’s a traffic policy.

7.) Quantized Serving Bakeoff
Build: Same model in FP16 vs AWQ/FP8; publish quality vs latency vs VRAM tradeoffs.
Why: Optimization without benchmarks is cosplay.

8.) Secure Multi-Tenant Inference Layer
Build: Tenant isolation, keyed access, sandboxed tool/exec paths, audit logs.
Why: One noisy neighbor (or leak) ends the B2B deal.

9.) Checkpointed Distributed Training Job
Build: Ray or Spój/Slurm job with FSDP/tensor parallel, fault injection, and resume-from-checkpoint.
Why: Training infra is judged on recovery not the happy path.

10.) Speculative Decoding Prototype
Build: Draft + target model path with prefix caching / chunked prefill; measure accepted-token rate.
Why: Speed wins are product features when you own the stack.

11.) GPU Partitioning Lab
Build: MIG (or equivalent) slices with fair scheduling, resource limits, and contention tests.
Why: Real clusters share silicon. You must prove fairness under pressure.

12.) Triton / Custom Kernel Path
Build: One hot path accelerated (attention, sampling or tokenizer-adjacent) with before/after metrics.
Why: Senior infra energy = knowing when frameworks aren’t enough.

13.) Observability Spine for Non-Determinism
Build: Traces for queue time, prefill, decode, cache hits, errors alerts on drift and cost spikes.
Why: You can’t page what you can’t see.

14.) Multi-Region Failover Drill
Build: Active-passive or active-active inference with DNS/failover and RTO/RPO notes.
Why: “We have GPUs” isn’t reliability. Failover is.

15.) Public Benchmark Teardown
Build: Publish an architecture + latency/cost report others can reproduce.
Why: Hiring managers trust public artifacts more than résumés.

Most people watch tutorials. Builders ship systems.

Bookmark & Repost.

[lumxss @bkdgiffug]

被裁之后，把整个求职流程交给 AI，最后拿到了 AI 工程师 offer。

这个 AI Job Search 可以从岗位匹配、定制简历、求职信，到模拟筛选、修改简历一路自动跑完，最后生成可直接投递的 PDF。

整个流程本地运行，个人资料不用交给第三方。GitHub 已经 3 万+ Stars。

如果你不想重复海投、改简历，可以看看这套思路。
🔗 https://t.co/GxDuUtEUEE

[INC Hyderabadi @_INCHyderabadi]

Hyderabad’s AI ecosystem is not just growing in research and startups, it is also building the talent needed to power the next wave of AI.

IIT Hyderabad has launched a four-week Applied AI Professional Certification Programme for working professionals, focused on moving beyond simply using AI tools to actually designing, building, evaluating and deploying AI systems for real-world applications.

The programme takes a practical “learn, apply and build” approach, with participants developing a production-style AI agent while learning areas such as RAG, multi-agent systems, AI evaluation, orchestration, observability, guardrails and deployment.

Sessions will be conducted in a hybrid format, combining IIT Hyderabad faculty-led classes, hands-on assignments, mentoring and project work, with the programme culminating in project demonstrations.

This is another sign of how Hyderabad is building the complete AI ecosystem, research, industry, innovation and now future-ready talent, all coming together in one city.

#HyderabadRising

[Jyoti Soni @soni_jyoti_]

BREAKING: Remote jobs in USD are now open to everyone.

There are millions of remote jobs out there.

Here are 9 sites to get a remote job that pays in USD:

[Gaurav Sen @gkcs_]

The AI Engineering Cohort starts this week.

The course material is built from cited research papers by Stanford, OpenAI, Google, and other leading research groups.

The cohort will include live classes, reading materials, and relevant resource links.

Students will have lifetime access to the course material.

We are a team of five AI Engineers working on the cohort. Our aim is to explain important AI concepts in a structured, easy-to-understand manner.

You can register here: https://t.co/lsqvTnnZG6

Happy 2026!

[Dammy @OlatunjiAyokan2]

Micro1 just changed their AI interview.

I cracked the code. Here are the exact answers they are looking for: 🧵

[Avinash Singh @AvinashSingh_20]

This pdf contains complete DSA resources at once place : https://t.co/kSAmz5f305

Bookmark it!

[మన ఆంధ్ర @AndhraFact]

యార్లగడ్డ లక్ష్మి ప్రసాద్:చివరకి ఇతను కూడా జగన్ రెడ్డి మీద కాడి పారేసాడు😂

PAYTM కుక్కలు ఇతన్ని తిడతారు ఏమో.ఇతన్ని మించిన చంద్రబాబు వ్యతిరేఖి ఉండడు
##
జగన్ రెడ్డి దుర్మార్గపు మైండ్ సెట్ ఎంత దారుణం అంటే కోట్లట పెడితే తప్పక క్యాడర్ పార్టీ వెంట ఉంటారు అనే రూట్ లోనే పోతున్నాడు

HMTV

[Jensen Huang @JensenHuang]

The mighty A100 fleet are mission-capable from 2020 through 2029. NVIDIA computing is more than chips. CUDA gives developers and NVIDIA engineers a common platform to continually upgrade Ampere, Hopper and Blackwell throughout their useful lives.
CUDA makes NVIDIA computing versatile. Versatility makes it fungible. Fungibility drives utilization and extends durability, making NVIDIA compute a productive asset: rentable, durable and financeable.

[Jensen Huang @JensenHuang]

This is a big moment for computing and @NVIDIA. We’ve made the leap from building chips to creating a new investable asset class: AI factory infrastructure. Every company will be powered by it. Every country will build it.

Thanks to Larry Fink of @BlackRock, Jon Gray of @blackstone, Bruce Flatt of @Brookfield, David Solomon of @GoldmanSachs, Jim Zelter of @apolloglobal, and Waldemar Szlezak of @KKR_Co for joining me in this historic partnership to build the infrastructure of the AI industrial revolution.

[RTV @RTVnewsnetwork]

జిమ్ వద్దు.. ఇంట్లోనే 20 కిలోలు తగ్గిన ఇల్లాలు!

ముగ్గురు పిల్లల తల్లి నిహిరా అగర్వాల్ జిమ్‌కు వెళ్లకుండా ఇంట్లోనే 8 నెలల్లో 20 కిలోలు తగ్గి అందరినీ ఆశ్చర్యపరుస్తోంది.

ఎలాంటి ట్రైనర్ లేకుండా బేసిక్ డంబెల్స్‌తో రోజూ 40 నిమిషాలే వర్కౌట్ చేసింది.

వారంలో 4 రోజులు వెయిట్ ట్రైనింగ్, 2 రోజులు కార్డియో చేసి ఒక రోజు రెస్ట్ తీసుకుంది.

భంగిమల్లో తప్పులు రాకుండా ఫోన్‌లో వీడియో రికార్డ్ చేసి చూసుకునేది.

హెల్తీ డైట్‌తో పాటు రోజుకు 500-750 క్యాలరీలు తగ్గిస్తే జిమ్ లేకుండానే ఎవరైనా ఇలా సన్నబడొచ్చని నిపుణులు చెబుతున్నారు.

#WeightLoss #Fitness #HealthyLifestyle #HomeWorkout #HealthTips #Transformation #RTV

[Amit Shekhar @amitiitbhu]

Learn LLM internals step by step - from tokenization to attention to inference optimization.

- Tokenization
- Byte Pair Encoding
- Math behind Attention - Q, K, and V
- Math behind √dₖ Scaling Factor in Attention
- Causal Masking in Attention
- Math Behind Backpropagation
- Math Behind Cross-Entropy Loss
- Decoding Transformer Architecture
- Feed-Forward Networks
- Batch Normalization vs Layer Normalization
- KV Cache
- Paged Attention
- Flash Attention
- Speculative Decoding
- Continuous Batching
- Prompt Caching
- Prefill vs Decode: LLM Inference Optimization
- Mixture of Experts Explained
- Grouped Query Attention
- Math Behind RoPE (Rotary Position Embedding)
- RMSNorm (Root Mean Square Layer Normalization)
- LoRA: Low-Rank Adaptation
- Large Reasoning Models (LRMs)
- Reinforcement Learning from Human Feedback (RLHF)
- Harness Engineering

Learn here: https://t.co/I78K8FSaJH

[Mahax @Mahaximus_]

Andrew Ng just dropped the best 1-hour course on Graph Engineering: from zero to full automation

00:00 - What is graph engineering
03:05 - Building a graph
13:58 - Multi agent architecture
22:57 - Live build: Google ADK
01:06:02 - Why graphs are the future

This 1-hour watch replaces any $500 course you could pay for

Watch it, then try your first graph with the step-by-step guide below

[Mahax @Mahaximus_]

Anthropic engineer:

"You're not supposed to prompt Claude. You're supposed to build a graph that runs itself."

In 30 minutes she breaks down how Anthropic builds graphs that remember, learn from their mistakes, and get smarter with every run.

The difference between an agent and a graph: one runs once, the other gets better every time.

Watch it, then read the full guide on graph engineering below.