System Prompt

Peter

System Prompt is a podcast about what’s actually happening in AI. Not hype. Not surface-level takes. We break down how AI is changing software, SaaS, infrastructure, and the way systems are built focusing on real-world tradeoffs, architecture decisions, and where the value is actually shifting. If you’re building, deploying, or thinking seriously about AI, this is for you.

  1. 5d ago

    How to Pick the Right AI Model?

    Episode 32: How to Pick the Right AI Model | DeepSeek vs Qwen Evals You've seen how an AI agent is built, configured, and given a job. But how do you know if the model behind it is right for the work? In Episode 32 of System Prompt, Peter and Val close out their agent series with a live demonstration of AI evaluations, or "evals." Instead of trusting public benchmarks or model reputation, they test the tasks that matter to a real workflow. Using smevals, they compare DeepSeek V4.1 Flash running locally across two NVIDIA DGX Sparks against Qwen 3.8 27B. Their tests cover structured data extraction, instruction following, reasoning, and coding. The smaller Qwen performs surprisingly well. But the real lesson comes from unexpected failures: DeepSeek adds a timezone to an otherwise correct answer, while Qwen initially returns no output on a task it passes when rerun. Peter and Val examine the outputs, question the grading rules, and troubleshoot live rather than presenting a polished happy path. They also explore how harnesses handle model behavior, why larger models aren't always necessary, and how to connect performance, hardware costs, and business ROI. The goal isn't to choose the biggest model. It's to choose one that can reliably do your job at a sensible cost. IN THIS EPISODE • Why benchmarks don't always represent your workload • How smevals uses tasks, runners, graders, and reports • DeepSeek V4.1 Flash vs Qwen 3.8 27B • Structured data, instruction following, reasoning, and coding tests • Diagnosing grading mismatches and missing responses • Why repeatability and failure severity matter • Harness engineering and deterministic validation • Local hardware costs, model selection, and business ROI TOOLS & RESOURCES smevals — Open-source evaluation framework: https://github.com/prime-radiant-inc/smevals KEY TAKEAWAYS BENCHMARKS AREN'T YOUR WORKLOAD Test against your real inputs, constraints, and expected outputs—not a leaderboard alone. NOT ALL FAILURES ARE EQUAL A formatting mismatch, empty response, or strict grader doesn't automatically prove a model can't do the task. Investigate the cause. SMALLER CAN BE GOOD ENOUGH For routine tasks, a 27B model may meet requirements that don't justify the cost of running a much larger model. EVALUATE THE EVALUATIONS Graders, runtimes, and model outputs can all vary. Rerun tests and examine failures before drawing conclusions. THE HARNESS MATTERS Prompts, tools, validation, and deterministic logic shape the reliability of the full system—not just the model. ROI STARTS WITH THE WORKFLOW Understand the task, its cost, and the value of automating it before deciding which model to deploy. CHAPTERS 00:00 Closing the Agent Series: Picking a Model 01:19 Why Evals Matter 04:52 Introducing smevals 07:33 DeepSeek vs Qwen 09:25 Running the Evaluation Suite 12:04 Structured Data Results 14:23 Instruction-Following Failures 15:49 Investigating a Grading Mismatch 18:29 Failure Severity and Impact 19:48 Deterministic Output Handling 21:14 Harness Engineering 23:03 Models vs Agent Harnesses 25:19 Investigating Qwen's Missing Response 26:28 Repeated Evaluations 27:21 Rerunning Tests 28:57 Human Bias vs Data 29:41 Coding Results 32:01 When Smaller Is Good Enough 34:56 Productivity and ROI 37:55 Understanding Business Problems 40:09 Wrap-Up

  2. Oct 23

    Setting Up Hermes Agent: From Configuration to Automation

    You've seen what goes into an AI agent. Now what does it take to actually configure one and give it a job? In Episode 31 of System Prompt, Peter and Val move from the architecture of Hermes Agent to a hands-on walkthrough of its Windows desktop interface. With Hermes already installed and connected to DeepSeek V4.1 Flash running locally across two NVIDIA DGX Sparks, they explore the settings that shape an agent's behavior, access, memory, and execution. They work through model selection and fallbacks, chat personalities, workspace configuration, approvals, file checkpoints, context compression, providers, tool integrations, and plugins. Not every feature needs changing, and not every experiment works as expected. That's part of the demonstration: no prearranged happy path. Then they give Hermes a real task: create a scheduled 8 a.m. AI news briefing, add sentiment analysis, inspect sources and output, and request a more polished PDF format. The conversation expands into what makes an automation useful—from daily operational reporting and ad analytics to finding RFPs and preparing proposals. The question isn't just what an agent can do. It's which steps in your existing workflow are worth handing off. • Configuring Hermes through the Windows GUI • Primary models, reasoning settings, fallbacks, and auxiliary models • Personalities, attachments, interface settings, and workspaces • Command approvals, secret redaction, file checkpoints, and browser access • Persistent memory and context compression • Agent step limits, subagents, and local versus cloud providers • Search tools, custom integrations, MCP, skills, and plugins • Testing web access with a locally hosted model • Creating an automated AI news brief with sentiment analysis • Applying agents to real business workflows KEY TAKEAWAYS CONFIGURATION IS PART OF AGENT ENGINEERING Choosing a model is only the beginning. Workspace access, approval rules, memory, tools, and execution limits determine how an agent operates. LOCAL DOESN'T MEAN UNRESTRICTED A locally hosted model can use web tools and run workflows, but access to browsers, files, credentials, and commands still needs deliberate controls. YOU DON'T NEED TO CHANGE EVERY SETTING Some defaults are fine. Understand what each setting governs, then adjust it based on the task and the risk. A WORKING DEMO ISN'T A FINISHED EVALUATION Hermes creates a schedule and generates an initial briefing, but repeatability, source quality, and output consistency still need testing. START WITH YOUR WORKFLOW, NOT THE TOOL Look at what you retrieve, compare, update, email, and report each day. The valuable agent is the one that removes real work from that process. CHAPTERS 00:00 From AI Infrastructure to a Working Agent 02:29 Models, Reasoning, and Fallbacks 03:30 Chat Personalities and Attachments 05:39 Appearance and Desktop Preferences 07:14 Workspaces, Approvals, and File Safety 08:44 Memory, Compression, and Voice 10:51 Advanced Settings and Agent Limits 12:00 Providers, Local Models, and Gateways 14:00 Tool Integrations, Plugins, and Skills 16:55 Can Hermes Build Its Own Tools? 18:24 Testing Web Access 19:20 What Should You Actually Do With an Agent? 21:12 Building a Scheduled AI News Brief 28:18 Adding Sentiment Analysis 30:58 Agent Personalities and Human Interaction 33:45 Reviewing the Briefing and Sources 37:20 Requesting a PDF Output 39:01 RFPs and Business Automation Opportunities 41:59 Finding Workflows Worth Automating 47:05 Wrap-Up and What's Next

  3. Oct 14

    Anatomy of an AI Agent

    READ THE FULL EPISODE PAGE: https://devmesh.tech/podcast/anatomy-of-an-ai-agent An AI agent isn't just a model with tools. What actually happens between your prompt and a completed task, and what keeps the system from falling apart when something goes wrong? In Episode 30 of System Prompt, Peter and Val take apart the Hermes Agent harness to examine the software engineering behind an AI agent. Building on their episodes about AI hardware and inference runtimes, they follow the agent loop from context assembly and model calls through tool execution, persistence, security, recovery, and observability. Hermes is the example, not the endorsement: the point is to show where probabilistic model decisions end and deterministic software takes over. They explore why switching model providers involves more than changing an API endpoint, why a tool call is a request rather than an action, and how seemingly ordinary components like SQLite, file-based memory, and approval rules support a sophisticated system. Along the way, they discuss evaluation, concurrency, prompt caching, local execution risks, and the trade-offs that come with every architecture. The agent loop may be simple. Making it durable, safe, and useful against real systems is the engineering challenge. • The model versus the harness: who decides what • The core agent loop and turn management • Context construction, caching, and token costs • Provider normalization across APIs and local models • Tool registries, validation, approvals, and execution • Persist-before-execute and crash recovery • Iteration limits, errors, and fallback behavior • Memory, skills, and what deserves to be remembered • Security boundaries and dangerous-command detection • SQLite, concurrency, observability, and evaluations • Hermes' strengths, limitations, and engineering trade-offs KEY TAKEAWAYS THE MODEL REASONS. THE HARNESS GOVERNS. The model proposes tools, arguments, and responses. The harness determines what can run, where it runs, what gets persisted, and which actions require approval. A TOOL CALL IS NOT EXECUTION The model can request an action, but deterministic code validates the request and enforces permissions before a tool actually does anything. PROVIDER COMPATIBILITY IS MORE THAN AN ENDPOINT Reasoning formats, message structures, streaming, and error handling differ. Normalizing those differences is substantial engineering work. RECOVERY IS BUILT FROM ORDINARY SOFTWARE Session leases, database writes, iteration controls, and error handling make a simple model/tool loop survivable when processes crash or requests fail. SECURITY IS AN ENGINEERED BOUNDARY Instructions alone aren't permission controls. Approval states, restricted execution environments, and configuration determine what an agent can actually do. EVERY ARCHITECTURE HAS TRADE-OFFS Hermes uses practical building blocks, but memory growth, provider complexity, local permissions, and SQLite concurrency still require deliberate choices. CHAPTERS 00:00 Intro: Anatomy of an AI Agent 04:15 Why Is the Hermes Repository So Large? 06:33 Agent Loop, Turn State, and Context Assembly 10:47 Specialized Harnesses and Context Costs 12:35 Provider Abstraction and Tool Registries 17:46 Who Actually Executes a Tool Call? 19:41 Approvals, Failures, and Evaluations 23:48 Recovery, Memory, and Skills 27:35 Deterministic Security and Execution Boundaries 31:21 Concurrency, SQLite, and Observability 36:25 Testing, Prompt Injection, and Memory Decisions 40:43 What the Model Decides vs. What the Harness Decides 45:24 Hermes Limitations and Engineering Trade-Offs 50:32 Final Takeaways: What Makes an Agent a System 52:02 Wrap-Up and What's Next

  4. Oct 10

    AI Is Making SaaS Optional

    READ THE FULL EPISODE PAGE: https://devmesh.tech/podcast/saas-vs-ai-part-2-revenge-of-the-swe SaaS isn't dead. But what happens when the people who know a business best can build the software they need themselves? In Episode 29 of System Prompt, Peter and Val return to SaaS vs. AI for an unscripted sequel: Revenge of the SWE. They debate whether agents become interfaces to existing SaaS products or replace some of those products entirely. From an accountant building tools with Claude Code and SQLite to small businesses recreating only the features they use, the question isn't simply whether AI can write code. It's who understands the process, validates the outcome, and maintains the result. The conversation explores software engineering, domain expertise, deterministic workflows, harnesses, MCP, AI operations, local inference, and the challenges of organizational data and persistent memory. They also discuss frontier subscriptions versus local models, AI devices, and why AI-generated answers still need scrutiny. This is a riff, not a prediction that every SaaS company disappears. The cost of code is changing. The cost of judgment is another story. • SaaS as the engine behind agents, or something agents replace • Domain experts building their own software • Claude Code, SQLite, and small internal applications • CRUD applications and custom alternatives • Engineering beyond writing application code • Deterministic workflows, validation, and TDD • Harnesses, MCP tools, and specialized agents • AI Ops: runtimes, hosting, access, and reliability • Local inference economics and AI hardware • Data quality, context, memory, and relevance • Questioning AI-generated plans and answers KEY TAKEAWAYS THE AGENT MAY REPLACE THE INTERFACE OR THE APPLICATION Val sees agents operating SaaS through integrations. Peter asks when a business can skip a product and build only what it needs. Both may happen, depending on the workflow. DOMAIN EXPERTISE CHANGES THE EQUATION An accountant or operations lead knows the requirements and exceptions. AI can lower the implementation barrier, but verifying the result still matters. THE COST OF CODE HAS COLLAPSED. THE PRICE OF JUDGMENT HAS NOT. Generating software is easier. Deciding what should exist, whether it works, who can access it, and how to maintain it still requires engineering judgment. AI OPS IS STILL SOFTWARE ENGINEERING Someone must own runtimes, harnesses, integrations, memory, evaluations, and infrastructure, whether inference is local or hosted. MEMORY IS NOT JUST STORAGE Saving information is one thing. Knowing what is relevant, outdated, or worth retrieving for a particular task is harder. CHAPTERS 00:00 Intro and an Off-the-Cuff Episode 01:05 Anthropic, AI Sentience, and the Pope 03:20 SaaS vs. AI: The Debate Returns 08:17 When the Accountant Becomes the Builder 11:41 Does AI Change the Need for UI/UX? 14:00 Deterministic Paths and AI Costs 17:36 The Value of Domain Expertise 19:02 Local Models and the Future of Copilot 22:18 Harnesses and Specialized Agents 26:16 AI Devices and Distributed Inference 30:37 AI Ops, Data Quality, and Validation 34:03 AI Gadgets Versus Your Phone 37:37 Small Businesses and Bespoke Software 41:36 CRUD, Rails, and SaaS Alternatives 45:40 The Cost of Code vs. the Price of Judgment 48:41 Have We Solved AI Memory? 53:24 SaaS, Copilot, and the Cost of AI 58:28 Local Models and What's Next 1:01:01 Closing Thoughts

  5. Oct 1

    Before You Host AI Locally, Watch This

    READ THE FULL EPISODE PAGE :https://devmesh.tech/podcast/youve-bought-ai-hardware-now-what Buying AI hardware is only the beginning. In Episode 28 of System Prompt, Peter and Val follow up on Before You Buy AI Hardware, Watch This by moving from planning into the actual hosting layer. Using GLM-5.3-Flash across two NVIDIA DGX Sparks with vLLM, they break down what the serving command is doing, which settings actually matter, and why hosting a model is very different from simply loading one. The conversation covers tensor parallelism, distributed execution, RoCE and NCCL networking, Docker, model mounts, KV cache, context length, concurrency, Mixture-of-Experts execution, chat templates, reasoning and tool-call parsers, DFlash speculative decoding, prefill, decode, and live serving metrics.Then they start the real cluster, bring the worker online before the head node, watch the model load across both Sparks, and run GLM-5.3-Flash through a coding workload. The live metrics show speculative decoding acceptance changing with the task, with generation throughput moving from the teens into the 30s and 40s as the workload becomes more predictable. • What happens after you buy AI hardware • Hosting GLM-5.3-Flash across two DGX Sparks • vLLM and distributed inference • Tensor parallelism across multiple devices • RoCE, NCCL, and cluster communication • Docker, model mounts, and runtime configuration • Context windows and KV cache • FP8 KV cache vs. FP16 • Concurrency and max active sequences • Mixture-of-Experts execution • Chat templates, reasoning parsers, and tool-call parsers • DFlash speculative decoding • Prefill, decode, and generation throughput • Why real workloads have to be tested QUICK CORRECTION At one point I say the ConnectX-7 link is 200 gigabytes per second. I meant 200 gigabits per second, or 200 Gb/s. Yes, I know the difference. It was a live recording, I misspoke, and I am correcting it here. KEY TAKEAWAYS RUNNING A MODEL IS NOT THE SAME AS HOSTING ONE Loading the weights is step one. Hosting adds networking, scheduling, memory management, concurrency, cache behavior, runtime compatibility, and observability. THE MODEL IS ONLY ONE PART OF THE STACK Runtime, quantization, KV cache, speculative decoding, context limits, scheduling, and hardware topology all affect system behavior. YOU DO NOT NEED TO ENGINEER EVERY LAYER YOURSELF Start with a known-good path, understand the major knobs, test it on your hardware, and go deeper only where the workload proves you need to. SPECULATIVE DECODING ONLY HELPS WHEN THE DRAFT IS RIGHT When DFlash acceptance is low, speculative work can become overhead. When the workload becomes more predictable, acceptance and generation throughput can rise significantly. THE WORKLOAD DETERMINES THE CONFIGURATION There is no universal best setup. Context, concurrency, precision, cache size, and speculative decoding all need to be tested against the actual task. CHAPTERS 00:00 Intro and the Follow-Up 03:23 Two DGX Sparks, vLLM, and GLM-5.3-Flash 07:00 Breaking Down the Hosting Command 11:00 Model Mounts, DFlash, and Runtime Patches 14:12 NCCL, RoCE, and Distributed Networking 17:30 API Behavior, Tool Calls, Reasoning, and Chat Templates 21:40 The vLLM Knobs You Actually Tune 25:45 FP8 KV Cache and Memory Tradeoffs 27:30 DFlash and Speculative Decoding 31:38 Starting the Two-Node Cluster 41:51 Running GLM-5.3-Flash 45:56 Reading the Live Metrics 50:17 Why You Have to Test 54:22 Coding Workloads and Higher Draft Acceptance 59:37 What This Means for Businesses 1:04:01 Closing Thoughts

  6. Sep 25

    Before You Buy AI Hardware, Watch This

    READ THE FULL EPISODE PAGE https://devmesh.tech/podcast/before-you-buy-ai-hardware-watch-thisMost businesses do not need more AI hardware. They need a better understanding of the workload they are actually trying to run. In Episode 27 of System Prompt, Peter and Val break down inference runtime, the layer that sits between the model and the hardware serving it, and why runtime decisions matter before a business starts buying GPUs, DGX systems, or other local AI infrastructure. The conversation covers Ollama, llama.cpp, vLLM, SGLang, quantization, context windows, KV cache, prefill, decoding, batching, speculative decoding, and parallelism. But the bigger question is business capacity. How many users will actually be active at once? Which departments need the most inference? When does demand spike? What should run locally, and what should stay in the cloud? Using examples from a 150-person company to enterprise GPU deployments, Peter and Val argue that infrastructure should be sized from real usage data, not assumptions. A business may not need 150 concurrent sessions just because it has 150 employees, and it may not need more hardware when better scheduling can solve the problem. • What inference runtime actually is • Ollama, llama.cpp, vLLM, and SGLang • Why concurrency should be measured instead of guessed • How quantization affects memory, performance, and quality • Context windows, KV cache, prefill, and time to first token • Why continuous batching matters • Speculative decoding and runtime optimization • Local devices vs. shared inference • Why expensive hardware can sit mostly idle • How businesses should pilot before scaling KEY TAKEAWAYS AN UNTESTED CONFIGURATION IS WINDOW DRESSING If a model, runtime, quantization, and hardware configuration has not been tested against the real workflow, it is still an assumption. Production decisions should be based on measured behavior. HEADCOUNT IS NOT CONCURRENCY A 150-person company does not automatically need infrastructure for 150 simultaneous sessions. Real usage depends on departments, workflows, working hours, and demand patterns. RUNTIME CHANGES HOW HARDWARE GETS USED Batching, caching, scheduling, memory management, and parallelism all affect how efficiently requests turn into useful GPU work. START SMALL AND COLLECT REAL DATA Pilot with one department or workload, measure throughput and bottlenecks, then scale from evidence instead of buying for theoretical maximums. IDLE CAPACITY IS STILL WASTED MONEY A powerful GPU cluster does not create ROI by existing. If expensive accelerators sit idle most of the day, the infrastructure may be oversized or poorly scheduled. THE BUSINESS PROBLEM COMES FIRST Before choosing a model, runtime, or piece of hardware, define what the system needs to accomplish, how often it will be used, and what level of performance the workflow actually requires. CHAPTERS 00:00 Intro and 20,000 Views 01:51 Why Businesses Are Exploring Local AI 03:25 What Inference Runtime Actually Is 06:51 Real Concurrency vs. Headcount 10:29 llama.cpp, GGUF, and Quantization 14:13 An Untested Configuration Is Window Dressing 15:22 Context and KV Cache 17:06 vLLM and Continuous Batching 18:28 Prefill, Decode, and Time to First Token 21:12 Prefix Caching and Runtime Optimization 25:17 Speculative Decoding 29:05 Engineering for Capacity 30:47 Local Devices vs. Shared Infrastructure 35:09 When Local Inference Makes Sense 39:53 Idle Hardware and Wasted Spend 48:10 Scheduling Inference by Department 49:40 The Full AI Infrastructure Stack

  7. Sep 18

    Your AI Isn’t Dumb. It’s Missing Context.

    READ THE FULL EPISODE PAGE https://devmesh.tech/podcast/your-ai-isnt-dumb-its-missing-context Most people do not have an AI problem. They have a context problem. In Episode 26 of System Prompt, Peter and Val break down why AI gives generic answers when it is handed generic information, and how a structured context document can turn the same model into something much more useful for real business work. Using a cloud-services account example, they compare three approaches: a short paragraph with a vague question, the same paragraph with a better prompt, and a full client context profile built from account, usage, support, contract, and relationship data. The difference is not subtle. The model goes from plausible advice to specific, evidence-backed next steps tied to the actual customer. The conversation also gets into where that context should come from, how teams can combine CRM data, tickets, emails, usage metrics, notes, and subject-matter expertise, why humans still need to verify the final document, and how this can replace long cross-functional information-gathering meetings with a much tighter workflow. • Why AI gives generic answers even when the model is good • What a business context document actually is • Why better prompting helps — but only up to a point • How CRM, ticketing, email, usage, and contract data fit together • How structured context changes the quality of account recommendations • Why every risk and recommendation should be tied to evidence • How context documents can become a living source of truth • Why account executives and technical teams still need to verify the data • How prompts encode what your business actually cares about • Why context engineering matters more than performative prompting • How AI can reduce cross-functional meeting overhead • Why many AI opportunities are process-shaped business problems KEY TAKEAWAYS BETTER MODELS DO NOT FIX MISSING CONTEXT A strong model can still only reason from what you give it. If the information is thin, ambiguous, or disconnected, the output will usually be generic. PROMPTING HELPS, BUT CONTEXT CHANGES THE GAME A better prompt improves structure and discipline, but a full context document gives the model enough evidence to produce specific risks, opportunities, questions, and next steps. THE CONTEXT DOCUMENT SHOULD BE A LIVING SOURCE OF TRUTH Customer history, usage, support issues, contracts, upcoming changes, relationship details, and decisions should evolve with the account instead of being rebuilt from scratch every time. HUMANS STILL OWN THE FACTS AND THE DECISIONS AI can synthesize information, but the people closest to the account still need to confirm the numbers, correct drift, and decide what actually matters. THE REAL ROI IS PROCESS COMPRESSION The value is not that AI “takes over” the work. It can compress hours of gathering, reconciling, and summarizing information into a much shorter review process. CHAPTERS 00:00 Why Context Matters 02:00 What a Context Document Is 07:59 The Generic Account Example 12:48 Better Prompt, Better Output 16:40 Building the Full Client Context Profile 21:01 What Changes With Complete Context 26:19 Replacing Long Cross-Team Meetings 29:37 Context + Prompt = Business Logic 32:12 Process Problems, Not Just Technical Problems 33:52 Guardrails, Verification, and Workflow Design

  8. Sep 9

    The Reality of Physical AI w/ Hiten Sonpal

    READ THE FULL EPISODE PAGE https://devmesh.tech/podcast/the-reality-of-physical-ai-with-hiten-sonpal Physical AI gets a lot more serious when a bad answer can move thousands of pounds of machinery. In Episode 25 of System Prompt, Peter and Val sit down with Hiten Sonpal, CEO of RISE Robotics and a robotics veteran whose career spans iRobot, defense robotics, autonomous lawn care, Electric Sheep, and heavy industrial machinery. The conversation gets into what changes when probabilistic AI meets deterministic machines, why safety envelopes matter, and why Hiten believes the future of robotics is more likely to be specialized machines that amplify people than general-purpose humanoid replacements. Hiten also breaks down RISE Robotics’ Beltdraulic technology, the SuperJammer robotic arm that lifted more than 7,000 pounds, where AI is actually useful in engineering today, and why impressive technology still does not become a business until customers want it and will pay for it. • What changes when AI starts moving physical machines • Human-in-the-loop robotics and deterministic safety systems • Why AI should not have unrestricted control of heavy equipment • RISE Robotics’ Beltdraulic alternative to hydraulics • The SuperJammer arm and its 7,000+ pound world-record lift • What Beltdraulics does better — and worse — than hydraulics • How RISE uses AI for software, sourcing, and calculations • Why robotics has a harder training-data problem than language models • Desirability, viability, and feasibility as tests for real innovation • Why Hiten is skeptical of humanoid-robot hype • Specialized robots versus general-purpose human replacements • What software engineers misunderstand about hardware • What robotics engineers underestimate about modern AI • Which jobs robotics is most likely to automate first KEY TAKEAWAYS PHYSICAL AI NEEDS DETERMINISTIC BOUNDARIES A model can make high-level decisions, but safety-critical machinery still needs hard constraints. The system has to know when the answer is simply “no.” SPECIALIZATION BEATS HUMAN REPLACEMENT A robot does not need arms, legs, hands, and twenty degrees of freedom if the task can be solved with wheels and a purpose-built mechanism. Complexity has to earn its place. HARDWARE CHANGES THE FAILURE MODEL Software bugs can often be patched quickly. Hardware failures involve parts, lead times, prototypes, shipping, installation, and sometimes redesigning the machine itself. NOVELTY IS NOT A BUSINESS A technically impressive product still has to solve a problem customers care about, at a price they will pay, with economics that work. AI IS ALREADY HELPING ENGINEERING — WITH CHECKS RISE is using AI to accelerate software work, research components, and assist with calculations, but Hiten is clear that engineering outputs still need human verification. CHAPTERS 00:00 Physical AI Gets Real 02:21 What Is Different About Robotics Now? 03:18 Failure, Safety, and Human Control 06:22 Should AI Directly Control Heavy Machinery? 09:03 Robots, Jobs, and Human Amplification 12:11 Why Robotics Needs a Hardware Revolution 13:05 The Origin of Beltdraulics 14:46 What Beltdraulics Does Worse Than Hydraulics 18:05 Building a 7,000-Pound-Lift Robotic Arm 20:42 How RISE Uses AI 24:11 The Robotics Training-Data Problem 27:01 Novel Technology vs. Real Business 28:52 The Problem With Humanoid Robots 36:06 The iRobot Lawn-Mower Story 39:58 Which Jobs Robotics Takes First 41:57 What AI People Misunderstand About Robotics 44:26 What Robotics Engineers Underestimate About AI 46:04 The Robotics Problem That Still Is Not Solved

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About

System Prompt is a podcast about what’s actually happening in AI. Not hype. Not surface-level takes. We break down how AI is changing software, SaaS, infrastructure, and the way systems are built focusing on real-world tradeoffs, architecture decisions, and where the value is actually shifting. If you’re building, deploying, or thinking seriously about AI, this is for you.

Information

  • Creator
    Peter
  • Years Active
    2026
  • Episodes
    32
  • Rating
    Explicit
  • Copyright
    © 2026 Peter
  • Show Website
    System Prompt