James Cooper

I heard a few people are suspecting this live demo showing a call to a virtual agent is not real https://lnkd.in/gnBiGDfd I can tell you that the call was real and we are able to allow the users to talk to the virtual agent with a text UI while still on the call. To find more, search the newly launched feature called 'Call Companion' under Dialogflow

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We just shipped an open source conformance test suite for Connect RPC and gRPC that ensures implementations follow the spec, and that Connect seamlessly interoperates with gRPC. 🚀 💯 We're proud to report that all Connect implementations that have reached v1 are 100% conformant, and we are committed to ensuring that conformance is required for future Connect implementations to reach a stable v1. 🏘️ The Connect conformance test suite establishes a clear quality standard that future Connect implementations in new languages can target, and it enables more community members to get involved in developing Connect. ✅ We take specifications seriously. These are the building blocks that many organizations rely on to develop their products. 👀 Check out our blog post for how we did this and how the official gRPC implementations fared in our tests. https://lnkd.in/gEz3nR6M

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What’s ahead for 2024? Elections in some of the most influential countries in the world including the US, India, Venezuela, Russia, South Africa, Taiwan, China and the UK. And yet we have zero handle on the use of generative AI, by legitimate or illegitimate players, in manipulating the electorates in how they will vote. That’s terrifying. In a small effort to equip myself with some strategies and frameworks for how to think through the use of AI in digital product design I took this course. It’s only a start but it’s an important one. Center for Humane Technology (CHT) remind us that it is possible, and economically viable, to develop products that help humanity rather just driving engagement. To anyone who is applying AI to products that will be out in our world, I recommend looking into the work of Tristan Harris, Randima Fernando and colleagues at CHT. So, that’s my hot take for the new calendar year. What’s shaping your thinking around AI?

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103 Early Hints is now GA and is fully supported in Ion, DSA, AAP and KSD. If you have personalized, dynamic, volatile and or long tail content, Akamai's implementation of 103 Early Hints is an effective way to improve performance. The production version of 103 Early Hints not only removes the limitations of the prototype, but also makes implementation easier without compromising flexibility for developers. Documentation: https://lnkd.in/empnQHuK #webperf #earlyhints

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To lift my reply up, I have expanded it here. These topics form much of the material on Day 2 of my Practical Messaging workshop next NDC Oslo 10-11 June) So the first thing to think about is the type of asynchronous conversation: * Messaging (uses a queue; transfer of control) * Eventing (uses a stream; publish-subscribe) (Replies can be either stream or queue, default to queue unless you are broadcasting). We also need to think about Provider over Producer/Consumer when thinking about the interaction. Who provides the API? The Consumer provides the API for messaging; the Producer provides the API for eventing. Concepts like In-Out, Out-Only are useful here from the old WS- Messaging specs. I use that taxonomy when I teach my workshop because it is still a useful tool. For example in In-Out we have a consumer that acts as the API provider and receives a message; it should use a queue; we have a response that probably travels back over that queue. The main issue that brings this up is causality - I need this step (taking a payment) to occur before this step (place an order). Definitely seen a few systems struggle with “event chaining” where they lose causality, particularly pernicious when a component fails (and can lead to nasty loops where work goes round and around - imagine folks keep turning up with the same order to your house). I spend time in my trying to avoid everyone reaching for a Process Manager (Saga) here. In a lot of cases I recommend what I call Turn-Taking which is essentially Pat Helland’s Activity model (components co-operate via their internal business logic).

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Check out my latest personal blog - on AV benchmarks!

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Samsara's latest report highlights how industry leaders are leveraging AI to boost efficiency and safety.  Dive into the full coverage to see how AI is transforming the way we operate:

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With the announcement of S3-native-streams (Freight clusters), here is a commentary on Confluent strategy regarding object storage, streaming and an open data architecture. https://lnkd.in/dTg6DKVy

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In the latest episode of the Mob Mentality Show, we dive into software development and team dynamics with Mark Shead. Discover how to revolutionize your team's productivity and create a more enjoyable work environment. https://lnkd.in/gV9R-sE4 https://lnkd.in/ggdUAFbX ### **Leveling Up the Entire Team** - **Maximizing Software Investment & Fun:** Learn how mob programming boosts software investment value and enjoyment. - **Team Size Strategies:** Debate whether to tackle problems with large teams or start with just 5 people. - **Real-Life Transformation:** Hear the story of five individuals transitioning from solo work to pair/mob programming and the positive outcomes including reduced cycle times, enhanced delivery rates, knowledge sharing, and collective code ownership.  - **Overcoming Silos:** Address the challenge of tying self-worth to personal knowledge silos and the sunk cost fallacy. - **Bottleneck Resolution:** Compare R&D bottleneck resolution in solo, isolated pair/mob, and dynamic pair/mob setups. - **Team Health:** Learn why the speed of asking questions and seeking help is crucial for team health. - **Sponsoring Risk Takers:** Discover the importance of sponsoring and encouraging team members who take risks and ask questions. - **Learning Vulnerability:** Hear Austin's baseball coach story on vulnerability and its relevance to software teams. ### **Effective Remote Teams** - **Solo Work vs. Collaboration:** Examine the norm of solo work and scheduled meetings leading to eight-hour days filled with meetings. - **Booking Meetings:** Understand the inefficiency of booking meetings weeks in advance just to get necessary information. - **Remote Team Room:** Learn about the remote team room approach that reduces cycle time for information sharing and fosters real-time collaboration. - **Video Call Dynamics:** Address the fear of being on video and the impact of informal versus formal video styles on zoom fatigue. ### **BDD & TDD: The State of Software Development** - **Roman Bridges Legend:** Reflect on the "hopefulness" in the software industry and the parallels with the legend of Roman bridges. - **Known vs. Unknown:** Delve into what is known versus unknown in software development. - **BDD/TDD Practices:** Discuss how much BDD/TDD should be integrated with the UI and the fear of creating test code that might need changes later. - **Evolving Product Code:** Become comfortable with evolving both product and test code. - **Unmatched Confidence:** Discover the confidence boost that comes with well-executed BDD/TDD. - **Safe R&D Spaces:** Balance the need for a safe time-boxed R&D experimental code with the certainty of test-driven development. - **Humoring the Coach:** Hear anecdotes about teams humoring their coaches with TDD, only to embrace it enthusiastically the next day.

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pgvectorscale: A Game-Changer for Vector Similarity Search 🚀 Timescale's #pgvectorscale extension for PostgreSQL has achieved a remarkable milestone, matching the performance of Pinecone, a dedicated vector database, while being 75% more cost-effective. 💰 In their latest blog post, Timescale showcases the impressive speed and efficiency of pgvectorscale, which leverages the power of PostgreSQL to deliver lightning-fast vector similarity search. This breakthrough opens up new possibilities for applications like semantic search, recommendation systems, and more. 🔍 Key takeaways: 🌟 pgvectorscale now matches Pinecone's performance in vector similarity search 💸 pgvectorscale is 75% more cost-effective compared to Pinecone 🔧 Easy integration with existing PostgreSQL databases 🌐 Scalable solution for handling billions of vectors ⚡ Optimized for fast and efficient search results pgvectorscale's seamless integration with PostgreSQL allows developers to incorporate vector similarity search into their applications with ease, while benefiting from the robustness and scalability of a proven database system. 🗄️ If you're looking for a high-performance, cost-effective solution for vector similarity search, pgvectorscale is definitely worth exploring. 🔍💡 #pgvectorscale #postgresql #vectorsimilaritysearch #semanticsearch #recommendationsystems #timescale https://lnkd.in/gsfgCK32

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Can you trust AI? 🤔 For most companies the answer is no - at least using current foundational models out of the box. ❌ Problems like sensitive data leakage and toxic content are major barriers to implementing production genAI solutions. The route towards trust lies in system architecture and the good news is the field is advancing rapidly, including increasingly easily accessible toolkits of solutions to increase trust in AI systems. 🔥 This is the hot area for new releases from major AI platform players (along with AI security), including AWS (https://lnkd.in/e45S-ehS), DataRobot (https://lnkd.in/eCWvppWR), and Databricks (https://lnkd.in/e6A7cTcH). 👨‍⚖️ Guardrail or judge models - leveraging a second AI model to police the content coming out of an original - are probably the most promising scalable solution. For many the best route is likely to be a new class of foundational models specifically designed for this task such as Llama Guard and Databrick’s new judge LLM. There is also the ability to fine tune a model (think a discriminator model from a GAN architecture) or use a general purpose foundational model. 👮‍♂️ Classic filtering techniques for sensitive data can also be applied to the result of an LLM, whether for PII, company IP, or inappropriate content. This can include regex, classic PII models (such as Amazon Comprehend), etc. 🚻 Human in the loop techniques can also be used, but are far more scalable when used to calibrate automated solutions such as those described above rather than manually verifying outputs in realtime. What’s still missing? 🔎 🎯 While techniques to improve accuracy and reduce hallucination such as RAG and AI agents have made great strides, there remains a big gap in conveying a model’s confidence in its output to end users. 💡One idea is to leverage the probability distributions used for top-p (nucleus sampling) at the token level instead at greater aggregations to convey confidence levels for entire model outputs or significant segments (e.g. each step in a chain of thought structure or AI agents framework). #ai, #artificialintelligence, #GenAI, #GenerativeAI, #llm, #datascience, #machinelearning, #mlops, #bigdata

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One of the challenges of incorporating AI into systems is that in some respects it's very similar to traditional software development, and in other respects it is not. Our software supply chain teams have been working for some time on how to translate software supply chain principles and practices to fit the needs of the AI supply chain. I'm very proud of the work our team and partners have done here. This report describes the key insights they have made. #ai #hardening

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New post on what happens after you add a control plane and local storage for observability: you get 1000x the telemetry when it matters. https://lnkd.in/gAvkk-qP

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The most intelligent Claude 3 model, with best-in-market performance on highly complex tasks. It can navigate open-ended prompts and sight-unseen scenarios with remarkable fluency and human-like understanding.

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<rant subject = #streaming and #queues> Streams have stronger ordering guarantees than message queues. Generally this means that all messages within a partition are read in the same order. This is highly useful when you need an ordered record of events that happened. As a consequence, streaming systems don't have individual message acknowledgement as it would violate ordering guarantees. Message queues have per-message acknowledgement, which means they have less strict ordering guarantees. One hybrid approach which is quite cool is "key-based subscriptions" in Apache Pulsar and also Confluent's Parallel Consumer for #apachekafka which guarantee order for all messages with the same key while allowing you to individually acknowledge messages. The tradeoff with individual message ack's is persistence (disk or memory)—you need to keep track of which messages have been acknowledged and which ones are still inflight. Pulsar uses BookKeeper to persist "subscriptions." Parallel Consumer stores them in the user-defined metadata in the consumer offset topic. There's an in-flight KIP for "Queues on Kafka" (KIP-932) but if I were the Grumpy Maintainer of Kafka, I would say that it's unnecessary given that it's already possible to implement queue semantics on top of Kafka in the client side (see: Parallel Consumer). Why add additional code bloat on the broker side when the features are already accessible? </rant>

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Large Language Models (#LLMs) are evolving rapidly, and the demand for running them at scale is skyrocketing. #Ollama is stepping up to meet this need with its official container image on Docker Hub, excelling in local #deployments. However, there’s a catch for containerized environments: downloading #models requires the ollama serve command to be running within the #container. This extra step complicates automated deployments via #CI #CD #pipelines, making seamless model access challenging. To take your #model to production, it needs to be #container-ready—a feature Ollama has yet to fully support. The gap in #AIOps for smooth deployments gets blurred with the inference services available as an easy way to take it to production. Goal is to overcome obstacles and deliver solutions 😉 so here is to get you started on having your (O)#llama #production ready in a private #deployment from your #local to #cloud all in #oneshot . Enjoy Medium: https://lnkd.in/g9jqBQ8d dev.to: https://lnkd.in/gi_gGwam #AI #LLM #Ollama #llama3 #phi3 #mistral #S6overlay #AIOps #CI #CD #oneshot #container #docker #podmanEnjoy

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Here's how to export a ARRL Field Day Cabrillo log from WSJT-X: https://lnkd.in/gE5HEyVE

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