Roland Schemers

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.

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🚀 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

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First story on what we have been up to at Augment. Much more to come! https://lnkd.in/gC6gbADA

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While there are certainly individual variations in skill and experience when discussing modern software development the story is much more nuanced than pure individual effort.  * software development is a team sport -- individual performance is heavily impacted by the system in which they are asked to operate in.  * we pair skill and experience to this model of understanding performance by expecting people to be able to influence and adapt in direct proportion to their seniority, aka the more senior you are the more responsible you are for the outcomes of your work, the less senior you are the more your manager and management chain are responsible.  * humans want to succeed, we're social animals who derive purpose from demonstrating skill, and working collectively. All of which is to say there is no way you have over 1000 people underperforming at your company without senior leadership deeply implicated in.  If nearly 10% of your company is failing, versus just having the market move against you, you need to fire the CEO.

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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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Looking to get started with in-database machine learning with MySQL HeatWave? This guide will help get you on the right path. https://lnkd.in/gPjuJU9g

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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.

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Fully Local LLMs with Web interface on your device: If you have free time to learn AND you have a Windows Desktop with an NVIDIA card, then my recommendation is to run Ollama and Open-WebUI in docker containers on a WSL Ubuntu24.04 Distribution. How do you do that?

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Wanted to give a quick shout out to Pump.co - they’ve helped us save ~60% on our monthly AWS bill for free. They also stopped our AWS credits from expiring 😁 It took 5 minutes to onboard and they have no access to our infrastructure so it's super simple to just have it run in the background and know that we're covered. If you spend more than $1000/month on AWS - or have credits that you don't want to lose - you should definitely spend 2 minutes just running an estimate to see how much they can save you: https://lnkd.in/gbw4dwh2 It's literally free money.

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Three things I appreciate about Stripe culture are: 1. Fondness for 20th century computer and technology aesthetics. 2. 99.999+% API success SLOs, and high security targets. 3. _Users first_ as a real living principal not just a slogan, whether it's a one-person microbusiness or Ford. All three are so charmingly displayed in https://bfcm.stripe.com/ So far today, before 8am on the east coast, it's showing our customers and merchants processed a bit over $5 billion, and 5,927 businesses are having their best day ever on Stripe (an all-time sales record.)

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Oso makes this look easy. Historically you either put all of your data into your auth system which is easy, but creates availability and privacy issues, or else you distribute your data which was scalable and secure, but leads to spaghetti authorization logic and complexity for developers. Such a nice balance here. The CAP theorem of authorization?!

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I was taking a look at AWS EC2 costs today and noticed something that seemed odd to me. It may be that I'm missing some details. Cost of storing 1TiB data in EBS: $80/month (https://lnkd.in/gn36ua-G) ---- Cost of transferring data out from EC2 to internet : $90/TiB (https://lnkd.in/gq-RP3J7) ---- Cost of transferring data in from internet to EC2: Free (https://lnkd.in/gq-RP3J7) Can anyone help me understand why outbound data transfers from "EC2 to Internet" are so expensive? Is there a technical reason I'm missing. Azure, Google Cloud, Alibaba and IBM have similar pricing numbers for outbound n/w transfers to the internet ($80+/TiB) However, if you consider cloud vendors like DigitalOcean (https://lnkd.in/g-TT3WiS) their outbound network costs are much more reasonable: $10/TB.

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This is super cool. The declarative approach OSO takes with authZ is the key to making this work. I can also quite easily see the integration with Wasp down the road :).

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LLMs are going to be commoditized akin to cloud services in the early 2010s. Big companies and startups alike are going to start building agents on top of these offerings. Whoever can solve the latency, guardrails and security issues will come out on top.

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The FireHydrant acquisition of Blameless signals a major shift in observability and incident management. My Partner Jaya and I just wrote about the looming "observability crisis"—how the explosion of data and tools has paradoxically made it harder to extract meaningful insights. This deal is a direct response to that crisis. By combining forces, FireHydrant and Blameless aim to build an end-to-end incident management platform to reimagine root cause analysis from the ground up. But to *truly* address the observability crisis, it will take more than consolidation. We need AI-driven systems that: → Provide natural language interfaces to query complex systems → Automatically generate and update incident response playbooks → Offer predictive maintenance by recognizing patterns across heterogeneous data sources → Synthesize post-mortem reports, distilling insights from terabytes of data Most critically, we need to ditch volume-based pricing models that incentivize data hoarding over problem-solving. Expect more moves as the market evolves to meet the demands of increasingly complex enterprise systems.

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Are you going to Amazon Web Services (AWS) re:invent in few weeks? I’m excited to join Antony Vance, Principal Engineer at Intel, and Jon Handler,Senior Principal Solutions Architect at AWS, where we’ll demystify GenAI and empower you to build and deploy cutting-edge applications. In our session, we’ll explore the Open Platform for Enterprise AI (OPEA), a collaborative framework that simplifies GenAI deployments. You'll also learn how to optimize Retrieval Augmented Generation (RAG) pipelines on AWS, leveraging the power of Intel® Xeon® processors and the OpenSearch Project.​ Register for this session to learn how Intel and OPEA are shaping the future of AI.​ Register here: https://lnkd.in/gsUUt6n7 ​ #IAmIntel #AWS #reInvent #GenAI  

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Shameless rip from Austin Parker’s BlueSky. But this whole thread is just too good! If you are passionate about #observability you’ll love this! Find the rest here! https://lnkd.in/gsvS_Jpz “”” 1. Metrics are good, actually, but most of you use them very poorly. … 4. there are no silver bullets when it comes to telemetry. you're trading between speed of access, data resolution, and retention … 5. you are not going to build a better datadog on-prem, they have a twelve year and twenty billion dollar head start … 18. the actual goal of observability is to have the data you need to find the answer rather than to have the data that contains the answer … 32. if you have thousands of dashboards, you have no useful ones … 44. single panes of glass are for glass cleaner, not o11y “””

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