Abhishek Chauhan

Very interesting post on Intuit's use of LLMs to manage K8s, via Aditya Kulkarni at InfoQ: "Intuit Engineering's Approach to Simplifying Kubernetes Management with AI" https://lnkd.in/eRN6_sAK

2

➰ APIContext's latest insights on cloud-based API performance have been featured on Nordic APIs! In the 2024 report, we delve into crucial aspects affecting API efficiency: 🔐 Authentication is key. Ensuring robust authentication mechanisms in APIs is crucial, yet offloading this function can introduce complex challenges. 📖 DNS configurations matter. How you handle DNS settings can significantly impact API performance, particularly when tied to authentication processes. 🌐 The role of third parties. Critical third-party services play a significant role in 🔗 API transactions, underscoring the need for careful integration and monitoring. ⛅ Not all clouds are created equal. Differences in cloud services can drastically affect API performance, an essential consideration as APIs account for over 80% of all internet traffic. #APIs #CloudComputing #TechInsights #APIContext

15

1 Comment

🚀 Want to run your very own trained LLM, privately and securely? 🤖🔒 Imagine harnessing the power of a Language Model tailored just for you without worrying about third-party servers! 💼✨ Try, LM Studio. Jest for few click . LLM running in your PC. With LM Studio, you can ... 🤖 • Run LLMs on your laptop, entirely offline 📚 • Chat with your local documents (new in 0.3) 👾 • Use models through the in-app Chat UI or an OpenAI compatible local server 📂 • Download any compatible model files from Hugging Face 🤗 repositories 🔭 • Discover new & noteworthy LLMs right inside the app's Discover page #LLM #private #chatgpt #AI

9

Since we launched Redshift Streaming Ingestion with Kinesis Data Streams (KDS) and Managed Streaming for Apache Kafka (MSK) we have seen great adoption with thousands of customers ingesting data at very high rates with low latency directly to Redshift Managed Storage. One common request was to extend the ingestion capabilities to other Kafka sources. Today we are extending Redshift's Streaming Ingestion to support self-managed Apache Kafka on EC2 as well as partners, such as Confluent Cloud. Go and Ingest! https://lnkd.in/gpzC-JD2 #amazon #redshift #streaming #ingestion #kafka #confluent

202

1 Comment

Pranay Prateek from SigNoz shares his thoughts on Unified Observability and how Open Source Signoz can help you to harness OpenTelemetry (OTEL) for Traces, Metrics at Logs. This talk took place at Open Source Observability Day #opensource #observability #otel

25

1 Comment

Hey everyone, I am an investor and advisor to Katanemo.... They are reinventing the infrastructure primitives for GenAI and launched Arch - https://lnkd.in/gCrbbkEV - an intelligent prompt gateway. Give the project a look on Github, and star the project if it resonates

88

3 Comments

Gwen (Chen) Shapira talks about Nile’s architecture in this amazing podcast from Kaivalya Apte. Things we have built and things we plan to build. Lots of details Why Postgres? How tenant isolation works? How is compute isolated? How is storage isolated? What parts of Postgres are reused? Can multiple tenants exist in a single transaction context? https://lnkd.in/ehH5J2e3

32

Want to build your own Retrieval Augmented Generation (RAG) applications? Dive into our latest blog post for a step-by-step guide using Ray, LangChain, and HuggingFace. Discover how to harness the power of these tools to create intelligent, context-aware AI systems. #AI #RAG #LangChain #HuggingFace #GoogleCloud

155

4 Comments

Folks I've spoken to about Amazon have likely heard me talk about the importance of the PR/FAQ process, a.k.a. "the working backwards process". This blog posts gives a very nice example of a PR/FAQ. https://lnkd.in/edTeuVRQ. Note that the blog doesn't capture the presumably 100s of reviews/discussions/rewrites that this doc represents, which is a part of the real value. When the process is done it all looks so simple, but getting a large organization to make a decision to something simple and do it in a BIG way is the challenge the fun of it all.

63

3 Comments

Worth reading: How the Apache Arrow Format Accelerates Query Result Transfer by Ian Cook One of the hidden problems of intersystem communication is transcription of data formats. Ian does a wonderful job of explaining the "why" of Apache Arrow as well as the "how" of its advantages. Read on...

8

A super clear, straight forward example of how to do cost effective RAG using LangChain for your document pipeline and Groq for compute resources. The code demonstrates how to speed up RAG and reduce costs using quantized embeddings (reducing 32 bit values to 8) Nice job Sahibpreet Singh! https://lnkd.in/gf5QMCKK

43

2 Comments

Today we are announcing support for Customer Resources in our Azure Monitor Managed Prometheus offering. Azure Monitor Managed Prometheus is a component of Azure Monitor Metrics, allowing you to collect and analyze metrics at scale using a Prometheus-compatible monitoring solution, based on the Prometheus project from the Cloud Native Computing Foundation. This fully managed service enables using the Prometheus query language (PromQL) to analyze and alert on the performance of monitored infrastructure and workloads. Now customers are able to scrape metrics from more resources. For example, a large organization in the Public Sector Banking and Financial Services and Insurance (BFSI) has used this capability to extra health and performance metrics from NVidia GPUs and visualize insights in Azure Managed Grafana. This integration facilitates a comprehensive understanding of GPU consumption patterns, enabling more informed decisions regarding optimization and resource allocation. You can read more here: https://lnkd.in/gEtdaKHr

127

Satya Nadella sparked a new (or reignited) debate about death of apps as a result of AI agents. While he makes a great point on the Agentic AI architecture and how traditional architectures will be obsolete, wondering if front-end apps will go away. Often, tools like Excel or well-designed user interfaces help me identify the right questions to ask. Can AI alone replace the value of that interactive discovery process? What do you think? #ArtificialIntelligence #AI #AgenticAI #AIApplications #FutureOfApps https://lnkd.in/g8UKWQmf

8

1 Comment

Can KV cache optimization go beyond just reducing memory footprint? Our latest work, SwiftKV, does exactly that—cutting prefill computation by half and significantly accelerating enterprise inference workloads! We’ve open-sourced the SwiftKV-optimized model checkpoints for the Llama 3 family and vLLM implementations, enabling efficient inference and making it easier than ever to integrate these advancements into your applications. - Tech blog: https://lnkd.in/gZbPBum3 - Paper: https://lnkd.in/g__9NpBd We hope SwiftKV inspires new possibilities for enterprise LLM efficiency for you!

461

5 Comments

Observability at the infrastructure and application levels must be harnessed to deal with today’s demanding workloads in #GenAI. Come talk to us to see how you can use the new AI with observability data to optimize your services.

17

JFrog reported its quarterly earnings on Aug 7th. ProntoNLP compared the JFrog earning call transcript to 3 other similar SAAS (DevOps) companies as well as to the average of the software GICS group. ProntoNLP's Aspect based LLM signal gave an early warning for JFrog on May 9th 2024 (their last earning call) when the Aspects score dropped from 0.52 to 0.4. ProntoNLP analyzed the earning call transcripts of these 4 SAAS (DevOps) companies JFrog, Atlassian, Dynatrace and Datadog. As can be seen in the image below the only company that had an increase in the LLM score compared with its score last quarter is Atlassian while JFrog had a much lower LLM score (-16%) compared with their last quarter (Color Legend is at the top part of the image below). The dotted navy blue line shows the average LLM score for each of the last 4 quarters for the whole software GICS group. All these 4 companies are SAAS companies and hence the customer related LLM categories are extremely important. The image below shows that Datadog and Atlassian have extremely high values for Customer Acquisition, Customer Behavior and Customer Retention. ProntoNLP's new LLM Aspects based signal for these companies shows the following trend: JFrog: a huge decline from 0.4 last quarter to 0.08 Atlassian: score stayed the same at 0.43 Dynatrace: slight decline from 0.45 last quarter to 0.42 Datadog : slight decline from 0.47 last quarter to 0.42 Based on the LLM scores in the Customer related categories and the change in the Aspects scores of the 4 software stocks it seems that Atlassian and Datadog are the top picks now and JFrog is at the bottom of the pack. The new comparison feature of ProntoNLP is extremely powerful and can support picking the winners and losers within any GICS group based on the latest #GenerativeAI technologies. ProntoNLP provides via SFTP real time quantitative signals on earning calls, filings, macro and news. Reach out to ProntoNLP to start using our #GenerativeAI based tools and Maintain an edge in alpha creation. #Quant #HedgeFunds #ArtificialIntelligence #StockMarket

7

This is incredible news for Amazon Kinesis Data Streams On-Demand users! KDS On-Demand now allows writing up to 10 GB/s, a significant 5x enhancement! The most remarkable aspect is that there is no action required to leverage this feature. #Kinesis #data #BigData #streamingdata #Datastreaming #ondemand #AWS Link: https://lnkd.in/gdm_FXrU

47

2 Comments