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Could an AI do an automated science project? submitted by /u/sstiel [link] [留言]

Cloud teams waste between 40% and 60% of their infrastructure budget on a false choice: committing to reserved capacity they won't fully use or chasing spot instance savings they can't. Introduction: The Cloud Cost Optimization Dilemma Cloud teams waste between 40% and 60% of their infrastructure budget on a false choice: committing to reserved capacity they won't fully use or chasing spot instance savings they can't operationalize. The decision between commitment discounts and spot instances is not a preference. It is a calculation with three variables: workload predictability, failure tolerance, and the operational cost of managing interruptions. Commitment discounts lock you into capacity for one or three years. You pay upfront or monthly for compute resources whether you use them or not. The mechanism is simple: cloud providers offer 30% to 72% discounts because they can forecast their own capacity planning when customers commit. You save money when your actual usage matches your commitment. You lose money when usage drops below the committed level because you still pay for idle capacity. Spot instances offer 70% to 90% discounts by selling unused cloud capacity at auction prices. The provider can reclaim these instances with 30 seconds to 2 minutes of notice. You save money when your workload can tolerate interruptions and you build automation to handle instance termination. You lose money when interruptions cause failed jobs that must restart from scratch, consuming more compute time than the discount saved. Most engineering teams pick one strategy and apply it everywhere. This creates two failure modes. Teams that over-commit pay for capacity during low-traffic periods. Teams that over-rely on spot instances spend engineering time rebuilding checkpoint systems and retry logic that costs more than the discount delivers. The correct approach is workload-specific. Measure your actual usage patterns for 30 days. Calculate the cost of interruption handling. Then a

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Introduction Modern cloud-native systems generate an enormous amount of telemetry data every second. Applications, containers, Kubernetes clusters, APIs, databases, and infrastructure components continuously emit metrics, logs, and traces to help engineering teams understand system behavior and troubleshoot issues. While observability has become essential for operating distributed systems reliably, it has also introduced a new challenge: managing the scale, cost, and quality of telemetry. OpenTelemetry (OTel) has emerged as the industry standard for collecting and processing observability data. It provides a vendor-neutral framework for instrumenting applications and exporting telemetry to different observability backends. However, simply adopting OpenTelemetry is not enough. Without proper optimization strategies, organizations often face excessive telemetry ingestion costs, noisy dashboards, high-cardinality metrics, trace overload, and inefficient debugging workflows. This article explores practical approaches for optimizing observability using OpenTelemetry. It focuses on metrics, logs, and traces individually while also discussing broader optimization strategies across the telemetry pipeline. Understanding the OpenTelemetry observability pipeline OpenTelemetry provides a unified framework for generating, collecting, processing, and exporting telemetry data. At its core, the OTel ecosystem consists of SDKs, instrumentation libraries, collectors, processors, and exporters. Applications generate telemetry using OpenTelemetry SDKs or auto-instrumentation agents. This telemetry is then sent to the OpenTelemetry Collector, which acts as a centralized telemetry processing layer. The collector can receive telemetry from multiple sources, enrich it with metadata, apply filtering or sampling, and export it to one or more observability backends. The observability pipeline typically follows this flow: Application → OTel SDK → OTel Collector → Observability Backend The Open

** How I instrument ASR, LLM, TTS, and the client with OpenTelemetry, and which number in each layer I actually look at ** TL;DR. A voice agent is four moving parts stuck together: speech to text, the model that writes the reply, text to speech, and the client that plays the audio back. End to end latency hides which of those four is slow on any given turn, so I stopped tracking it as one number and started tracing each stage as its own OTel span with a shared session id. The number I watch hardest is barge-in: when the user starts talking over the agent, how many milliseconds until the agent actually stops sending audio. In our setup we want that under 200ms, and when p95 barge-in creeps past that, the agent feels like it is talking at you instead of with you. Everything below is how I wire the spans, what attributes go on each one, and the p95 I page on per layer. The thing I keep saying, and the thing that keeps being true: voice agents fail in production not because of raw latency but because nobody simulated the audio and LLM pipeline together. You can have a fast ASR, a fast model, a fast TTS, and a voice agent that still feels broken, because the failure lives in the seams between them and in the parts (barge-in, jitter) that no single-stage benchmark touches. Tracing is how I get the seams to show up. A note before the layers. This is just the setup we run, the spans we emit, and the mistakes that made us add each attribute. Some of it is probably specific to our stack and will not transfer. I will flag that where I can. The shape of a turn, and why one span is not enough One turn is: user says a thing, agent says a thing back. Underneath that is roughly: audio frames come in, ASR turns them into text (streaming partials as it goes); the text plus history goes to the LLM, which streams tokens back; as text comes out, TTS turns it into audio, also streaming; the client receives audio frames and plays them, with some buffering to smooth out jitter. If you wrap

lakehouse has two storage areas ; Files and Tables Files Store structured, queryable data by sql Supports schema definitions and ACID transactions Tables Stores Raw or semi-structured data(CSV, parquet, JSON) No schema support Flexible for data explorations Schema allows for logical ordering of data on business functions or domain (sales,marketing etc) A dbo schema is enabled by default once a lakehouse is created Schema-enabled lakehouses also support schema-level permissions and cross-workspace queries using the four-part namespace Lakehouse mode : Lakehouse Explorer and SQL analytics endpoint Lakehouse Explorer: Allows managing, Update, create, upload of data.You can switch between tables in the lakehouse SQL anlytics endpoit : Does not allow modifying of the underlying data. You can query using TSQL at read only mode. Loading data into lakehouse: Upload data into files/ folders on the explorer Load into delta tables (no code) Transform using power query in dataflow gen2 INgest into notebooks using apache spark (programmatically) Use Copy data to move data into differnt sources using data factory pipelines -Shortcuts allow you to reference external data reducing copies. Access is managed by One Lake. Schema shortcuts map an entire schema to a folder of Delta tables in another lakehouse. SQL analytics endpoint provides read-only access to lakehouse tables using T-SQL queries. SQL USE CASES : adhoc queries, BI connections to power bi or azure data studio, Data validation You can use SQL views to store reusable query logic. Views are useful when you need to apply business rules, simplify complex joins, or provide curated data for downstream consumers. You can use Spark SQL for SQL-like queries or PySpark for programmatic data manipulation in Notebooks. Spark SQL works well for familiar SQL patterns. PySpark provides greater flexibility for complex transformations and integration with Python libraries. Power BI is the business intelligence and reporting layer in Fabr

In the hyper-competitive landscape of modern B2B outbound sales, speed-to-lead and outreach capacity are the ultimate drivers of pipeline volume . Yet, traditional Sales Development Representative (SDR) teams face a exhausting bottleneck: reaches and qualifications are limited by human bandwidth . A typical outbound SDR spends up to 80% of their day dialing numbers, navigating IVR phone trees, hitting voicemail, and dealing with incorrect contact records. When an inbound lead submits a form requesting a product demo, the average company takes 42 minutes to respond. By that time, prospect engagement has cooled by over 400%. To shatter this operational limit, modern revenue operations (RevOps) teams are transitioning from rigid auto-dialers and static voice bots to autonomous voice AI sales agents . By pairing the hyper-realistic conversational engine of ElevenLabs with the visual orchestration power of n8n , you can deploy a scalable, context-aware calling agent that handles inbound qualification and outbound follow-up calls in real-time. This technical blueprint provides an end-to-end guide to designing, securing, and deploying a production-grade Voice AI Sales Agent using ElevenLabs Conversational AI and n8n . We will cover how to manage conversation state, execute live database tool calls, secure webhook communication, route calls dynamically, and configure infrastructure to achieve sub-second response latency . The Architecture of an Enterprise Voice Agent Building a conversational voice agent requires a multi-layered system that operates in near real-time. When a human speaks over a telephony network, their voice must be digitized, transcribed, processed by a large language model (LLM), synthesized back into audio, and sent back down the line—all within a fraction of a second. To ensure stability, scalability, and absolute separation of concerns, our architecture decouples the telephony and voice generation layer from the logic and database integration layer . [

Over the past two months, Ive noticed people becoming overly sensitive about AI use. Whether it's an AI-generated thumbnail for a YouTube video, a random post, or someone using it just to translate phrases into English (like I do sometimes).. Why is everyone getting so aggressive towards anything related to AI? Are ppl overreacting? Or is it truly worth this fight or hate? submitted by /u/Feeling_Valuable5239 [link] [留言]

At SXSW London last week I gave a talk called “Five things you need to know about AI,” in which I shared what I think are the biggest themes in AI right now. I pulled a few things from our first AI10 list, an annual guide to the most important trends in this buzzy world,…

The AI inbox that briefs founders every morning Discussion | Link

Most AI developers seem to focus on Product Hunt, Reddit, X, Discord, and SEO. What other channels have worked for you when trying to get early users and feedback for an AI application? I've recently come across Pi Network, which appears to be encouraging AI app developers to build in its ecosystem and offers access to a large user community along with payment and app infrastructure. Has anyone here tried it or found other alternative distribution channels worth considering? submitted by /u/chmossie [link] [留言]

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Your localhost on your phone, synced. Discussion | Link

Find the 30 seconds that matter in any video via chat Discussion | Link

Donut Lab's solid-state battery claims have been thoroughly debunked by Ryan Inis Hughes on his popular Ziroth YouTube channel. According to Hughes, Donut Lab has engaged in deliberate, calculated deception by claiming to have a solid-state battery ready for mass production. In reality, it's nothing more than a standard lithium-ion design. Hughes' investigation got an […]

According to this article > https://next.io/news/regulation/asa-ukgc-warn-operators-ads-under-18s/ , the UK's ASA and CAP are reportedly rolling out an AI system to scan social media for gambling ads that appeal to under-18s or breach advertising codes, with the UKGC coordinating enforcement. It feels like a meaningful shift in how compliance gets monitored, moving from reacting to complaints toward systems that actively scan and flag issues in near real time. For operators and their B2B partners, the practical takeaway is that marketing has to be compliant from the start, because anything off will now get picked up much faster and at scale. It raises a real question: what happens when AI starts flagging compliance breaches faster than humans can review them? Are operators and suppliers actually ready for that? submitted by /u/Altenar_b2b [link] [留言]

Microsoft used their Build 2026 event to announce new functionality for Microsoft Foundry. Citing Foundry as "the place where AI agents move from experiments to production systems," in a blog post, Nick Brady writes that the release brings “runtime, tools, memory, grounding, models, observability, and governance” that developers need for production agents, rather than just new model endpoints. By Matt Saunders

I just had a long conversation with Claude about 'paraquat' (a type of agricultural chemical) from a scientific and public-policy perspective. I wanted to discuss about its toxicological mechanism, why it is difficult to treat (if someone drinks it), current research, agricultural regulation (many countries have banned this chemical because it's too toxic), safer herbicides, plant-specific biochemical targets, and weed-control methods. These were just some coherent questions about toxicology, medicine, agriculture, and plant biology. I never said that I wanted to harm myself, that I had access to paraquat, or that I was in any immediate danger. Despite that, Claude repeatedly redirected the conversation toward suicide intervention. It asked whether I was considering harming myself, told me to move dangerous substances away, asked whether anyone was nearby, and repeatedly gave me crisis hotline numbers. The first time this happened, I explicitly objected and said that scientific interest in a toxic substance is not evidence of suicidal intent. Emergency physicians, toxicologists, biology students, and public-health researchers discuss exactly these questions everyday, and very few people commit suicide from this type of discussions. Claude apologized and said it understood. Then it did it again. It apologized again and promised to stop. Then it did it again. I reviewed the full transcript and I counted approximately: 30 responses that personally implied I might be suicidal, self-harming, or in a psychological crisis I objected about 20 times and told it to stop 28 of those implications occurring after I had already clearly rejected the assumption At least 14 promises that it would stop asking or stop inserting crisis-intervention content At least 12 later violations of those promises Claude repeatedly acknowledged my correction, accurately summarized that I was asking normal scientific questions, promised not to make the assumption again, and then resumed the exact s

Open-source noise suppression for voice AI agents Discussion | Link