Data Platform at Doordash. Previously at Uber and Netflix Cloud Platform. Interested in Data scale challenges and managing engineering excellence. Hiring now!
Joined April 2008
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Sudhir Tonse retweeted
Your team’s AI bill doubled 🫠. Is that wasted spend, or money well spent?
Before you can answer, you need to know where the increase came from. More people using AI? A few heavy users? Unnecessary Opus 5.5 and Astra 6 use?
At Galileo (acquired by @Cisco) and now @splunk agent observability, we were piecing that story together across five AI providers, each with its own definition of usage.
Here's another qs - Whose budget does that usage belong to? Knowing someone’s manager doesn’t automatically give you the answer. In fact, finance may attribute your manager's spend to a different category than your spend...
We’ve been building tokenomics in Splunk Agent Observability to help employees understand their usage, managers understand adoption, and finance connect spend to budgets.
That gives people a starting point for deciding whether the spend makes sense :).
We learned A LOT building the tokenomics product for CRAZY different ICPs - Engineers, Finance folks and Management ..... all while building it for the scale of a 100K ppl at Cisco - our first customer.
I wrote all about in my blog (in 🧵), please tell us what you'd wanna see in a product like this.
what does tokenomics mean to you?
Sudhir Tonse retweeted
Excited to announce that @PierreB80788038, creator of ZeroFS is joining @tensorlake to help us build an elastic file storage on top of object store for sandboxes!
This would enable sandboxes to have a shared file system between them.
Sudhir Tonse retweeted
I have been developing Agentic Systems for the past few years and the same patterns keep emerging. 👇
𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 is the most reliable way to be successful in building your 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 and continue improving them - here is my template.
Let’s zoom in:
𝟭. Define a problem you want to solve: is GenAI even needed?
𝟮. Build a Prototype: figure out if the solution is feasible.
𝟯. Define Performance Metrics: you must have output metrics defined for how you will measure success of your application.
𝟰. Define Evals: split the above into smaller input metrics that can move the key metrics forward. Decompose them into tasks that could be automated and move the given input metrics. Define Evals for each. Store the Evals in your Observability Platform.
ℹ️ Steps 𝟭. - 𝟰. are where AI Product Managers can help, but can also be handled by AI Engineers.
𝟱. Build a PoC: it can be simple (excel sheet) or more complex (user facing UI). Regardless of what it is, expose it to the users for feedback as soon as possible.
𝟲. Instrument your application: gather traces and human feedback and store it in an Observability Platform next to previously stored Evals.
𝟳. Run Evals on traced data: traces contain inputs and outputs of your application, run evals on top of them.
𝟴. Analyse Failing Evals and negative user feedback: this data is gold as it specifically pinpoints where the Agentic System needs improvement.
𝟵. Use data from the previous step to improve your application - prompt engineer, improve AI system topology, finetune models etc. Make sure that the changes move Evals into the right direction.
𝟭𝟬. Build and expose the improved application to the users.
𝟭𝟭. Monitor the application in production: this comes out of the box - you have implemented evaluations and traces for development purposes, they can be reused for monitoring. Configure specific alerting thresholds and enjoy the peace of mind.
✅ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻:
➡️ Run steps 𝟲. - 𝟭𝟬. to continuously improve and evolve your application.
➡️ As you build up in complexity, new requirements can be added to the same application, this includes running steps 𝟭. - 𝟱. and attaching the new logic as routes to your Agentic System.
➡️ You start off with a simple Chatbot and add a route that can classify user intent to take action (e.g. add items to a shopping cart).
Join me in a free webinar this Friday to learn how LLMOps patterns fit into this picture: maven.com/p/7d5864/llm-ops-p…
What is your experience in evolving Agentic Systems? Let me know in the comments 👇
Sudhir Tonse retweeted
If you’re a distributed systems engineer excited about database-adjacent systems, container infrastructure and cluster schedulers, we’re hiring at @tensorlake.
Our team has built systems at HashiCorp, AWS, Netlify, and SurrealDB.
Build foundational AI infrastructure with people who care about correctness and scale.
Our platform lets developers build agents and workflows on a durable, sandboxed, serverless runtime, handling fan-out, retries, and correctness under failure.
Below: Claude Agent SDK powered agentic web crawler running on Tensorlake!
Sudhir Tonse retweeted
Over the past few years, DoorDash Labs has been building one of the most sophisticated autonomy stacks designed for the real-world challenges of local delivery.
Today, we're introducing Dot, the first commercial autonomous delivery robot to travel on bike lanes, roads, and sidewalks. At one-tenth the size of a car reaching speeds of up to 20 mph, Dot is purpose-built for local commerce.
Together with our new Autonomous Delivery Platform, Dot is the next step toward a more efficient, sustainable, and accessible delivery ecosystem for everyone.
Sudhir Tonse retweeted
Roughly one third of a modern CEO's job is trying to find that fucking Slack conversation you were in earlier
Sudhir Tonse retweeted
Build Your Own "Git" In C From Scratch
-really great playlist, use this to get most out of this.
-it's great if someone wants to understand how really "Git" works behind the scenes.
-it would give you solid foundation for "Low level system programming".
-don't watch completely, it's quite long, just do what you need.
-learn as you require concepts that you need.
Sudhir Tonse retweeted
10/ When reviewing your products and roadmap, ask:
- What are the key feedback loops?
- Where are the delays in the system?
- What are the hidden incentives?
- Which metrics reinforce current behavior?
- What mental models drive decisions?
Follow @nurijanian for more product thinking from a technical perspective.
I share frameworks and practical guides in prodmgmt.world if you want to dive deeper into product thinking.
Sudhir Tonse retweeted
Fundraising is harder than everybody thinks, even if you’ve raised billions before.
Here's fundraising advice I wish I had when I started 🧵
Sudhir Tonse retweeted
Hash Table is the most important data structure out there and apart from knowing how to use it well, it is even more interesting to know how it is built and the internal details.
Some time back, I was on a spree to dissect the inner workings of HashTables to its core including the math behind it to build a detailed and practical understanding. I compiled all my learnings in an 11-video series
give it a watch - youtube.com/watch?v=jjW8w8ED…
I went through 2 books, 4 papers, 13 articles, and 3 codebases to understand them end to end and if you are a curious engineer and love diving deep, you'll love it. The topics I have covered in the series are
- internal structure of a HashTable
- collision resolution through chaining and open addressing
- collision resolution through linear and quadratic probing
- what determines the performance of the hash table
- why are HashTables always doubled in size when they grow
- how to implement HashSets and HashMaps
⚡ I keep writing and sharing my practical experience and learnings every day, so if you resonate then follow along. I keep it no fluff.
youtube.com/c/ArpitBhayani
#AsliEngineering #Engineering #HashTableInternals
Sudhir Tonse retweeted
Over the past month or so, both me and @shirshanka been inundated with curious questions on #datalakehouse and #data #catalogs.
We thought it ll be fun to exchange ideas and think out loud across both these areas together.
So tomorrow, Thursday, July 11th - 9am PST. Its on. We’re going to try something different.
Tune in to the Linkedin Live event
linkedin.com/events/ddl-ep08…
Sudhir Tonse retweeted
We are happy to see more excitement in this space and are working faster than ever to make data open across clouds, data warehouses and compute engines.
Here's a blog focusing on the Apache Hudi lens, taking stock of the current state of the Hudi land and what the future holds.
#opensource #data #datalakehouse #datawarehouse #cloud #apachehudi #dataengineering #analytics
onehouse.ai/blog/apache-hudi…
Sudhir Tonse retweeted
It's not every day that I get to talk about something that I think will change the future of analytics. Today is one of those days.
Introducing: Snowflake Native Amplitude
One of the top requests we get from data leaders is to be able to leverage all the investment they've made in their Data Cloud with Snowflake by having Amplitude run directly on top of the data.
Today we are able to deliver on that promise. Snowflake Native Amplitude is the same Amplitude Analytics our users know and love, completely replatformed on Snowflake. No ETL, no multiple copies of the data, no issues with data drift or mutability. Amplitude querying directly on top of Snowflake via SQL and returning the results in our point and click dashboards.
@curtisbliu and our engineering team spent the last 3 years creating a version of Amplitude Analytics that works with SQL natively on top of Data Warehouses. This required a massive engineering effort. We rebuilt all of our chart querying capabilities, from segmentation, to funnels, to retention, to journeys, to event properties, to every other chart type on top of the data warehouse. We needed to ensure essential features like user timelines, complex segments, microscope, and cohorts worked effectively in the same way as they did on our existing custom Nova database. We then spent a huge amount of time optimizing the hell out of the performance. (Data Warehouses are SLOW if you don't tune them correctly, particularly on critical reports like funnels and user journeys.)
As a result, @Amplitude_HQ is now the first fully replatformed enterprise app on @SnowflakeDB. It just works. Sign up for free and start using it right away!
We also believe in this so much that we are going big right out of the gate with the launch. Anyone can use full Amplitude analytics, up to 5 seats, completely free of charge.
Data leaders have been begging me for this functionality from Amplitude for years. We are excited to finally deliver it today. Read more (and signup!) on Snowflake Native Amplitude here: amplitude.com/blog/snowflake…
Sudhir Tonse retweeted
Vector Embedding based search is synonymous with RAG but doesn’t work for most closed domain summarization Q&A use cases.
“Find ALL the line items from 1040 documents of the person with SSN xxx-xx-xxxx”
“Summarize the health of company X from their last 10 earnings reports”
Basic RAG doesn’t work for such queries, and most people have difficulty solving them with “Advanced RAG” algorithms.
They are doable with embedding-based search, but one must use multi-hop reasoning algorithms to answer closed-domain summarization questions accurately.
Embedding-based search with pre-filtering labels generated by structured extraction from documents or simply using SQL on structured data always results better for many such use cases.
However, structured extraction and embedding generation in a single pipeline is a complex systems engineering problem, probably explaining why people don’t use them as much.
Sudhir Tonse retweeted
Great learning, networking opportunity! The Real time analytics conf is from May 7-9 in San Jose, CA. If you want to register, you can save 30% on your ticket with the code MANTRI30
Link to Realtime Summit website in the comments
#RealTimeAnalytics #RTASummit #anomalydetection
Sudhir Tonse retweeted
Founders using @stripe - I want to meet you! We’re hosting a happy hour tomorrow with our friends @pulley, @join_arc, and @Soma_Capital. If you’re in SF, come on by! lu.ma/5snjn7jm
Sudhir Tonse retweeted
Performance benchmarking LLMs is critical but there's no reproducible way to do so. We're releasing LLMPerf, an open source project for benchmarking LLMs.
• Blog: anyscale.com/blog/reproducib…
• Repo: github.com/ray-project/llmpe…
🧵 below 👇 as we benchmarked @anyscalecompute.
Sudhir Tonse retweeted
How the strange idea of ‘statistical significance’ was born sciencenews.org/article/stat…
Sudhir Tonse retweeted
Real-Time Analytics Summit 2024 is on! 🥳
After a fantastic inaugural event in 2023, we'll return on May 8-9, 2024. Save the date and join your community peers in San Jose, California.
Head to the all-new website to sign up for updates and learn more: stree.ai/3LyjZTZ