If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/pf5AKPYepRAvi Chawla (@_avichawla):You're in an AI engineer interview at Apple.The interviewer asks:"Siri processes 25B requests/mo.How would you use this data to improve its speech recognition?"You: "Upload all voice notes from devices to iCloud and train a model"Interview over!Here's what you missed: https://t.co/uKIt7n1teK ...

Federated Learning - IBM provides a good video on Federated Learning [1]

Federated Learning Overview - Federated learning addresses the challenge of training ML models on private data residing on user devices [3] - It dispatches a model to end devices for training on private data and aggregates the trained models on a central server [4] - This approach reduces computation requirements on the server side by distributing most computation to user devices [3] Challenges in Federated Learning - Client devices have limited RAM and battery power, requiring efficient training methods [3] - Aggregating different models received from client devices to create a central model poses a challenge [3] - Privacy-sensitive datasets are often biased with personal likings and beliefs, leading to skewness in client data distribution [5] Data Privacy and Bias - Data on modern devices, such as images, messages, and voice notes, is mostly private [2][4] - The skewness in client data distribution, such as an overrepresentation of pet, car, or travel images, needs to be addressed [5] Application in Speech Recognition - Siri processes 25 billion (25B) requests per month, representing a large dataset for improving speech recognition [1] - The data on user devices can be leveraged to improve ML models [1]

RT Avi Chawla (@_avichawla)Big moment for Postgres!AI agents broke the idea of what a database is supposed to do.Traditional databases were built for humans, and Agents broke that model.- They branch endlessly.- They run ten experiments at once.- They need isolation, context, memory, structured reasoning, and safe sandboxes.Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior.Agentic Postgres is an agent-ready version of Postgres b ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/mCRlrHndB7Avi Chawla (@_avichawla):Big moment for Postgres!AI agents broke the idea of what a database is supposed to do.Traditional databases were built for humans, and Agents broke that model.- They branch endlessly.- They run ten experiments at once.- They need isolation, context, memory, structured https://t.co/VL0tm1lAPj ...

Here's the usage of their MCP server.It's created based on 35 years of Postgres knowledge, and full access Postgres docs, all in a format that agents can easily process.You can try this live in Tiger Data's Free Tier here: https://t.co/vQVHBNnYHW. https://t.co/RK3TqdKaOj ...

Big moment for Postgres!AI agents broke the idea of what a database is supposed to do.Traditional databases were built for humans, and Agents broke that model.- They branch endlessly.- They run ten experiments at once.- They need isolation, context, memory, structured reasoning, and safe sandboxes.Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior.Agentic Postgres is an agent-ready version of Postgres by @TimescaleDB that solves ...

You're in an AI engineer interview at OpenAI.The interviewer asks:"We're ready to launch GPT-5.How would you make sure it's secure and bias-free?"You: "I'll fine-tune it on safe datasets and validate outputs."Interview over!The post below explains what you missed:Avi Chawla (@_avichawla):OpenAI.Google.Meta.Everyone's facing the same problem with LLMs:How to prevent them from adversarial attacks via prompts.OpenAI even paid $500k in a Kaggle contest to find vulnerabilities in gpt-oss-20b.Why?Because despite ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/ly7ZBo29vdAvi Chawla (@_avichawla):OpenAI.Google.Meta.Everyone's facing the same problem with LLMs:How to prevent them from adversarial attacks via prompts.OpenAI even paid $500k in a Kaggle contest to find vulnerabilities in gpt-oss-20b.Why?Because despite evaluating LLMs against correctness, https://t.co/QIb6V28KgQ ...

Github repo: https://t.co/JHLsO3My9u(don't forget to star it ⭐) ...