Chris Caruso

Check out Michael Levan and Matthew Voget chatting about how #API development is changing. They highlight a killer application of #AI (and also how you can easily start building with OpenAI's API). Jump to minutes 10 - 13 for a great snippet on the typical complexities of API dev and testing that is automated away with Blackbird from Ambassador

11

A meta note about this talk: I am, as a lot of folks out there know, not exactly bullish on LLMs in general. I think most of the hype is nonsense—even while I think some of their capabilities are really astonishing!—but I do think they’re here to stay. So: First of all, if we’re going to be working in a world where people are authoring code with LLMs (Copilot etc.) and using them to “do things” as agent-based systems, the world of inputs to the models matters, as do the environments in which they run. They matter a *lot*! Second, I think a lot of engineering organizations pretty consistently underinvest in foundations, because of the simple realities that many kinds of foundations projects take longer and have a less direct/measurable connection to profitability. They just do, no way around it. Third, then, what are the ways that engineering leaders—including managers, but especially Staff+ engineers—can mitigate the risks of LLMs and improve the chances that they are net neutral or even positive, rather than negative, for software quality, UX, and business outcomes? Secretly (and yes, I am saying it out loud now!) it is predicated on my belief that THESE ARE THE THINGS WE SHOULD BE DOING ANYWAY… but this is a moment when we have a really obvious reason how and why these things matter, with which we can make that case to decision-makers. And because these are the things we should be doing anyway, an organization which uses this LLM-hype-moment to improve the foundations on which we run LLM-related systems… will be in a better spot two years from now *even if the LLM ecosystem craters*. Seems like a win.

10

👋 We just released v1.15 of DBOS Transact. This release includes several user-requested features. Now you can query the history of workflows, write pluggable/extensible event receivers, and manage workflow retries through the dead letter queue. Check it out here: https://lnkd.in/ge_hYQqt Let us know if you have any feedback or feature requests! Start building reliable applications with DBOS Transact today 💁‍♀️: https://lnkd.in/g7_mvEP7 #TypeScript #PostgreSQL #database

33

We call this self integration - Coming soon. Tyne integrations can utilize any API documentation, read all functions, create and execute code within minutes instead of weeks which reduces manual labor significantly. For example, we created 80 functions from the Spotify API on single thread in just 10 minutes, a process that typically takes weeks. With multi-threading, this could be even faster. #innovation #digitalmarketing #technology #creativity #Future #futurism #Entrepreneurship #Markets #Startups #Marketing #SocialMedia #VentureCapital #SocialNetworking #LeanStartups #Economy #Economics #sustainability #healthcare #education #design #sales #supplychain #retail #data #top #recent #best #LinkedIn #AI #Automation #Intelligence #Analytics #Artificial #Machine #startup

3

Embrace the future of technology with RSV system

2

A good friend recommended me to try Cursor.sh, so I did. I was not ready for what happened next. But first, let's start with a little idea I had for a while. Every time I needed to check a text, I needed to find my tab with ChatGPT, login if disconnected, start a new chat, type "fix typo:" then paste my text to fix, then copy the text back. Not efficient. I knew that using OpenAI APIs I could do a simple website that would do the same, but without the need of writing "fix typo:" every time. A simple hack. Of course I never found time to start this pet project. Until I decided to give it a try with Cursor.sh and made it real is a couple of hours. fix-typos.com

12

1 Comment

https://lnkd.in/eGNiPW2s This is a Notebook LM podcast experiment I created using the public domain text of Meditations by Marcus Aurelius. The source file comes from a free version of Meditations at Project Gutenberg (see: https://lnkd.in/eH9P-2cP), the first provider of free electronic books. This is the only source file used to generate this podcast. The summary generated by NotebookLM is as follows: The provided text is a biography of Marcus Aurelius Antoninus, Roman emperor from 161 to 180 A.D., followed by excerpts from his personal writings, known as the Meditations, and a selection of letters from the Roman rhetorician Fronto, who acted as Marcus Aurelius's tutor. The biography recounts the emperor's lineage, military campaigns, and personal life, while the Meditations offer insights into his Stoic philosophy, emphasizing virtue, reason, and the acceptance of fate. Fronto's letters provide a glimpse into the emperor's education and the relationship between tutor and student. Sources: Text via Gutenberg Audio file via NotebookLM Image via Google search Movie via iMovie Workflow: 1) Downloaded txt file from Gutenberg 2) Uploaded to new personal Notebook (https://lnkd.in/eQkiFABu) 3) Click Generate Audio Overview 4) Download .wav file 5) Create new iMovie file with image + audio + title 6) Export to .mov file 7) Upload to YouTube Entire workflow time: 22 minutes (including a complete first listening to the playback of the audio) I did chuckle at "a real dude with real problems..." #notebooklm #genai #google #ai #artificialintelligence #aiaudio #stoicism #stoicphilosophy #marcusaurelius

4

1 Comment

AWS re:Invent is less than two weeks away. Want to accelerate your AI initiatives? Let's connect in Vegas and discuss how Meibel enables your engineering team to build Explainable AI into your products and experiences.

55

New Time Series Benchmark compares CrateDB with MongoDB and Influx. CrateDB stands out with its very fast ingest performance and instant aggregation of any group of columns. No need for complex indexing strategies. In today’s digital landscape, efficient handling of time-series data is key for informed decisions in IoT, cybersecurity, and data-driven industries. A reliable database can make all the difference. #DataManagement #TimeSeries #BigData #PerformanceBenchmark #DataAnalytics #TechNews

8

learn best parts of continuum tool in one short demo

23

What does generative AI do to create new content? "Generative AI devours massive amounts of training data to learn underlying patterns and relationships, then generates entirely new content with a unique twist." Discover how generative AI sparks creativity by mimicking and remixing data into fresh, original content.

1

Very useful overview of the CDN market...

3

New Methods for Efficient Finetuning of LLMs!

10

These are the boring, yet impactful aspects of API operations I am here for--low-tech, economy impacting, real-world stuff like the API deprecation notice from the Department of Labor.

20

Tecton 1.0 has officially launched! Congrats to the team on this significant milestone. 1.0 is our first release with both predictive ML and generative AI capabilities. We built the first version of Tecton in 2019. It had minimal scope and capabilities, but we had a big vision for what it could become. Predictive ML was dominant back then, and structured data was the primary currency. With the rise of GenAI and LLMs, a new wave of intelligent use cases powered by unstructured data emerged. Tecton 1.0 helps users solve end-to-end data challenges across the AI lifecycle. It's a comprehensive AI data platform that provides inputs and context to models in production—features, tailored prompts, and domain-specific embeddings. See Michael Del Balso and Kevin Stumpf's post for more info: https://lnkd.in/e7mxwwkb

60

5 Comments

This right here. These are the moments you live for as an Engineer. Domo.AI is a game-changer. Our customers are amazed at what they can do. Raw data to critical business insights... in seconds. Bring on the #ai #revolution

65

6 Comments

I'm having enlightening convos with folks who deliver infra for others to run. It sounds like "runbooks/wiki docs" are a real problem from a handoff/support perspective. We have an elegant solution, when you deliver runbooks as "click to play/pre-configured" Notebooks, and a command fails, the user gets an `escalation button` that shares a link to the output/metadata into the shared Slack room. It's ideal for bridging the gap when there's a support relationship. 🤓 This sounds like a case study in the making.. anyone else struggling with this?

14