Joe Magdaleno

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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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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Anthropic released another paper on their ongoing work towards interpretability of large foundational models like their Claude 3 family, built on previous work by many. Their work continues to be enlightening and inspiring. The findings in this work are generally similar to previous findings on smaller models but scaled up to Claude 3 Sonnet: 💡 they find large numbers of "features" corresponding to combinations of neuron activations and these features map to concepts that we recognize/understand: for example, features for transit infrastructure, particular people, concepts (like bias and "objects lacking sentience"). This might sound familiar from classical machine learning, but the interesting thing is that there is no single thing/neuron encoding each of these concepts, it's a particular combination of neuron activations that embeds these concepts in "superposition" in the network. 💡they observe a sort of "distance" of these feature activations that arrange themselves "near" semantically similar features. When they use their sparse autoencoder approach to learn more and more features they observe that the level of abstraction in the detected features becomes more granular, in ways that are interpretable and meaningful. 💡when they artificially fire these activation patterns during inference, it affects the output in ways that relate to the "feature". For example, asking Claude "what's the most interesting science" normally it responds Physics, but if you clamp the feature activations for the feature "Brain Science" to 10x their max values then Claude responds: Neuroscience. Ways to interpret _and_ influence the model. Great readable work [1] along with an interactive explorer of some of the "features" themselves [2] 🎂 There's an easter egg too: if you scroll in the feature explorer tool and try and select one of the "Randomly selected features from 1M" you get a message "if you want to see the rest of the features, we're hiring!" with a link to their recruiting page :) [1] https://lnkd.in/gZbparwp [2] https://lnkd.in/gC9eus53

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HPC software has always been challenging to create & manage. The relatively young Chapel Language offers a unique approach to development for parallel computing.

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We started off originally building our Portfolio view for assets we extract from Brokerage statements. Today we've added another piece of the puzzle by bringing Private Investment holdings (Private Equity Funds, Syndications, JVs, etc) extracted from Capital Statements into our platform.

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The Langchain team has recently released a video and code demonstrating how to create your own research assistant. This system employs a multi-step decision process and allows for parallel execution, providing tangible and useful outcomes. While the approach remains quite semantic it can be an incredibly valuable resource for those looking to enhance their work efficiency.

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Project update! My application tracking extension is now doing fuzzier matching and injecting new information into LinkedIn (and other sites) to keep me from doubling up on applications. Next up: local storage and bulk import/export operations...

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A "culture of frugality" is great cloud architecture design principle. Every bit that goes through AWS has a cost, be sure you know what the cost implications are when designing a new system or feature. Another interesting takeaway from this article is switching to Fargate dropped compute costs by 86%! A serverless approach can be a great way to cost effectively manage varying load patterns. #aws #serverless #costoptimization #finops

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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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Had a chance to share some thoughts on securing code. We are in an era where nothing is trusted and must be signed to be trusted. This includes all libraries we embed and the depency chain that comes with it. Don’t miss out on more actionable strategies! Join us at Keyfactor Tech Days to hear Eric and other experts share how businesses can secure their future in the rapidly evolving cybersecurity landscape: https://okt.to/UAHgmz #Cybersecurity #DigitalTrust #Cryptography #QuantumComputing #KeyfactorTechDays #PQC

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I'm excited to share my latest article on LeadDev: "From False Urgency to Strategic Leadership." In this piece, I delve into the challenges and opportunities that come with shifting from reactive problem-solving to proactive strategic thinking. This article is designed to be a resource for staff+ engineers looking to enhance their leadership skills and drive meaningful impact in their role. 🔗 Read the full article here: https://lnkd.in/g7qxqE9r I hope you find it helpful and I’m eager to hear your thoughts and experiences. #Leadership #TechLeadership #LeadDev #FalseUrgency #StrategicLeadership

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Its about solving the problem, not 'having AI' in the system. This is from last March, but the Wave 1 vs. Wave 2 distinction does seem to be playing out. Less 'generate me content', more 'synthesize vast amounts of relevant data and aid in decisonmaking or signal extraction'. https://lnkd.in/ehFBFC32 They don't talk about multi-agent systems here, but looking forward to more startups with a focus on leveraging agentic design patterns (https://lnkd.in/ed5rMmFe) to streamline real world workflows.

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Clear and rich information on RAG variations. I think this will be useful to most practitioners who have gone beyond the intro and demo workflows, and now need to consider how to refine their flows.

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Highly recommend giving this one a read! Workflow contributor Sean Hughes gives a thoughtful breakdown of why closed GenAI foundation models should be of concern to everyone, and why open systems are the key to a safer, better future. Check it out. 👇

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Practical explanation of API’s the next time you are in a buzzword salad meeting… https://lnkd.in/e-HSTwC3

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The difference between a decision and a data-driven decision is like the difference between a shot in the dark and a carefully aimed arrow. Which decision do you want to make?

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