Podcast appearances and mentions of Kevin Ball

Stephen Goldsmith is in Nashville to see Sunderland take on Liverpool! He catches up with a number of Sunderland fans as well as club legends Kevin Ball and Kevin Phillips! Hosted on Acast. See acast.com/privacy for more information.

AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required. NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions. Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post NanoClaw and the Rise of Personal AI Agents appeared first on Software Engineering Daily.

AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required. NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions. Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post NanoClaw and the Rise of Personal AI Agents appeared first on Software Engineering Daily.

AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required. NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions. Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post NanoClaw and the Rise of Personal AI Agents appeared first on Software Engineering Daily.

AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required. NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions. Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post NanoClaw and the Rise of Personal AI Agents appeared first on Software Engineering Daily.

AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required. NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions. Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post NanoClaw and the Rise of Personal AI Agents appeared first on Software Engineering Daily.

AI agents have transformed how software gets written, but the operational side of running software in production has not yet experienced a similar revolution. The same teams responsible for keeping systems healthy, investigating incidents, and managing reliability are still doing much of that work manually. Mezmo is a Production AI company that makes autonomous operations fast, efficient, and safe. Their open source project, AURA, is a declarative agent framework specifically designed for SRE and platform engineering workflows. It takes a Kubernetes-inspired approach where teams define what they want agents to do rather than scripting every step of how to do it. Andre Elizondo is the head of product at Mezmo, and he has a background in systems engineering, SRE, and observability. In this episode, Andre joins Kevin Ball to discuss what makes SRE agent workflows fundamentally different from coding agents, how AURA handles context engineering, AURA’s declarative configuration model, the spectrum of agent autonomy, and where the role of the SRE is headed as agents take on more of the operational work. Full Disclosure: This episode is sponsored by Mezmo. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post AURA and Open-Source Agents for Production Operations appeared first on Software Engineering Daily.

AI agents have transformed how software gets written, but the operational side of running software in production has not yet experienced a similar revolution. The same teams responsible for keeping systems healthy, investigating incidents, and managing reliability are still doing much of that work manually. Mezmo is a Production AI company that makes autonomous operations fast, efficient, and safe. Their open source project, AURA, is a declarative agent framework specifically designed for SRE and platform engineering workflows. It takes a Kubernetes-inspired approach where teams define what they want agents to do rather than scripting every step of how to do it. Andre Elizondo is the head of product at Mezmo, and he has a background in systems engineering, SRE, and observability. In this episode, Andre joins Kevin Ball to discuss what makes SRE agent workflows fundamentally different from coding agents, how AURA handles context engineering, AURA’s declarative configuration model, the spectrum of agent autonomy, and where the role of the SRE is headed as agents take on more of the operational work. Full Disclosure: This episode is sponsored by Mezmo. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post AURA and Open-Source Agents for Production Operations appeared first on Software Engineering Daily.

AI agents have transformed how software gets written, but the operational side of running software in production has not yet experienced a similar revolution. The same teams responsible for keeping systems healthy, investigating incidents, and managing reliability are still doing much of that work manually. Mezmo is a Production AI company that makes autonomous operations fast, efficient, and safe. Their open source project, AURA, is a declarative agent framework specifically designed for SRE and platform engineering workflows. It takes a Kubernetes-inspired approach where teams define what they want agents to do rather than scripting every step of how to do it. Andre Elizondo is the head of product at Mezmo, and he has a background in systems engineering, SRE, and observability. In this episode, Andre joins Kevin Ball to discuss what makes SRE agent workflows fundamentally different from coding agents, how AURA handles context engineering, AURA’s declarative configuration model, the spectrum of agent autonomy, and where the role of the SRE is headed as agents take on more of the operational work. Full Disclosure: This episode is sponsored by Mezmo. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post AURA and Open-Source Agents for Production Operations appeared first on Software Engineering Daily.

Stephen and Rick are at the Fixtures Breakfast at the Beacon of Light, home of the wonderful Foundation of Light. They bring you their instant reaction to the fixtures and catch Kevin Ball, along with Foundation COO, Jamie Wright. Hosted on Acast. See acast.com/privacy for more information.

Most of the cryptography securing the internet today rests on mathematical problems that classical computers cannot solve in any reasonable timeframe. That assumption is now being tested. Recent advances in quantum computing have dramatically compressed timelines, and many in the industry have set a target of full post-quantum security by 2029, meaning a complete migration to algorithms designed to remain secure against quantum attacks. Bas Westerbaan is a cryptography engineer at Cloudflare, where he leads the company’s efforts to migrate to post-quantum cryptography. In this episode, Bas joins Kevin Ball to discuss how quantum computers threaten public key cryptography, what post-quantum algorithms actually are and how they work, the timeline shifts that have made quantum readiness feel so urgent, and what software engineers need to do now to prepare their systems. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Preparing for Q-Day appeared first on Software Engineering Daily.

Most of the cryptography securing the internet today rests on mathematical problems that classical computers cannot solve in any reasonable timeframe. That assumption is now being tested. Recent advances in quantum computing have dramatically compressed timelines, and many in the industry have set a target of full post-quantum security by 2029, meaning a complete migration to algorithms designed to remain secure against quantum attacks. Bas Westerbaan is a cryptography engineer at Cloudflare, where he leads the company’s efforts to migrate to post-quantum cryptography. In this episode, Bas joins Kevin Ball to discuss how quantum computers threaten public key cryptography, what post-quantum algorithms actually are and how they work, the timeline shifts that have made quantum readiness feel so urgent, and what software engineers need to do now to prepare their systems. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Preparing for Q-Day appeared first on Software Engineering Daily.

Most of the cryptography securing the internet today rests on mathematical problems that classical computers cannot solve in any reasonable timeframe. That assumption is now being tested. Recent advances in quantum computing have dramatically compressed timelines, and many in the industry have set a target of full post-quantum security by 2029, meaning a complete migration to algorithms designed to remain secure against quantum attacks. Bas Westerbaan is a cryptography engineer at Cloudflare, where he leads the company’s efforts to migrate to post-quantum cryptography. In this episode, Bas joins Kevin Ball to discuss how quantum computers threaten public key cryptography, what post-quantum algorithms actually are and how they work, the timeline shifts that have made quantum readiness feel so urgent, and what software engineers need to do now to prepare their systems. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Preparing for Q-Day appeared first on Software Engineering Daily.

Most restoration owners have a crazy story about how they got into the business, but very few have survived a first job so brutal it almost drove them out of the industry forever.In this episode of Restoration Pros Unplugged, host Clinton James sits down with Andrew Rickabaugh, co-owner of Rick and Ball Restoration and a One Tom Plumber franchise in Huntsville, Alabama. Andrew pulls back the curtain on his non-linear path to entrepreneurship, spanning roles as a paramedic, firefighter, Huntsville police officer, and a 20-year career in the Marine Corps. The real turning point, however, came at age 24 when he was hired into a restoration firm as an area manager, only to find himself leading an operational disaster. Handed a mountain of derailed reconstruction files, Andrew spent his days visiting furious homeowners and eating crow for mistakes made by completely unmanaged crews. The experience was so exhausting that he quit after 364 days, vowing never to set foot in the restoration industry again.The story didn't end there. Years later, a call from his brother-in-law and business partner, Kevin Ball, convinced him to return on his own terms to launch an independent company. Andrew breaks down how they put their heads down and did the dirty grunt work themselves, organically climbing the organic rankings to become the highest-rated restoration firm in a hyper-competitive market. He details their strategic expansion into the commercial market, explaining what it takes to find and manage a high-performing business development rep who isn't afraid to pick up the phone and build long-term relationships with property managers.To eliminate the expensive, margin-choking bidding wars over third-party plumbing referrals, Andrew and Kevin made the bold choice to buy a One Tom Plumber franchise. Now managing a team in the mid-30s with a fleet of 15 service vehicles, Andrew shares raw insight into how they handle modern operational constraints. He explains how they utilize AI tools to audit scopes and counter insurance adjusters using strict IICRC guidelines, and why they are aggressively pivoting toward a customer financing model to bypass carrier delays and compress their average 30-day reconstruction start times.Whether you are a military veteran looking to make the leap into home services or an established restorer feeling trapped as the bottleneck in your own business, Andrew's perspective on efficiency, tech integration, and self-reliance will reframe your growth strategy. He leaves listeners with a powerful reminder on the true value of entrepreneurship: if you don't own your business, you can't control your future.- Connect with Our GuestLearn more about Andrew and his team: www.rickandball.com- Scale Your Restoration BusinessWant to generate exclusive, high-intent water damage jobs and build an inbound marketing system you actually own? Book a free strategy session with the Water Restoration Marketing team: waterrestorationmarketing.com/discovery-call/

Software engineering has developed powerful tools for observability, data management, and continuous testing, but hardware engineering has largely not kept pace. The feedback loops, tooling, and infrastructure that software engineers take for granted simply do not exist in most hardware programs. Nominal is a data platform built to help hardware organizations move at the same speed as software teams. It manages the hardware data supply chain end to end, from ingesting high-frequency sensor data off physical assets to enabling real-time control room monitoring, post-test analysis, and simulation correlation. Jason Hoch is the co-founder and CTO of Nominal, and he has a background spanning distributed data systems at Palantir and cloud infrastructure at Vercel. In this episode, Jason joins Kevin Ball to discuss why hardware engineering has lagged so far behind software in tooling and observability, the unique data challenges of working with high-frequency time series sensor data, how Nominal handles both real-time control room workflows and post-test analysis, why AI agents are transforming software development but have not yet made the same leap in hardware, and what it would take to close that gap. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post The Hardware Bottleneck AI Can't Fix appeared first on Software Engineering Daily.

Software engineering has developed powerful tools for observability, data management, and continuous testing, but hardware engineering has largely not kept pace. The feedback loops, tooling, and infrastructure that software engineers take for granted simply do not exist in most hardware programs. Nominal is a data platform built to help hardware organizations move at the same speed as software teams. It manages the hardware data supply chain end to end, from ingesting high-frequency sensor data off physical assets to enabling real-time control room monitoring, post-test analysis, and simulation correlation. Jason Hoch is the co-founder and CTO of Nominal, and he has a background spanning distributed data systems at Palantir and cloud infrastructure at Vercel. In this episode, Jason joins Kevin Ball to discuss why hardware engineering has lagged so far behind software in tooling and observability, the unique data challenges of working with high-frequency time series sensor data, how Nominal handles both real-time control room workflows and post-test analysis, why AI agents are transforming software development but have not yet made the same leap in hardware, and what it would take to close that gap. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post The Hardware Bottleneck AI Can't Fix appeared first on Software Engineering Daily.

Software engineering has developed powerful tools for observability, data management, and continuous testing, but hardware engineering has largely not kept pace. The feedback loops, tooling, and infrastructure that software engineers take for granted simply do not exist in most hardware programs. Nominal is a data platform built to help hardware organizations move at the same speed as software teams. It manages the hardware data supply chain end to end, from ingesting high-frequency sensor data off physical assets to enabling real-time control room monitoring, post-test analysis, and simulation correlation. Jason Hoch is the co-founder and CTO of Nominal, and he has a background spanning distributed data systems at Palantir and cloud infrastructure at Vercel. In this episode, Jason joins Kevin Ball to discuss why hardware engineering has lagged so far behind software in tooling and observability, the unique data challenges of working with high-frequency time series sensor data, how Nominal handles both real-time control room workflows and post-test analysis, why AI agents are transforming software development but have not yet made the same leap in hardware, and what it would take to close that gap. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post The Hardware Bottleneck AI Can't Fix appeared first on Software Engineering Daily.

AI coding tools have dramatically accelerated the pace of development, and the bottleneck in the software development lifecycle has shifted to code validation and testing. However, the conventional tools and workflows that QA teams have relied on were not designed for a world where a single engineer can generate thousands of lines of code in a day. SmartBear is a software quality platform spanning test automation, API lifecycle management, and observability. The company recently launched an AI-native QA platform called BearQ, which deploys autonomous agents that explore web applications, learns their structure and behavior, and authors and maintains test cases continuously. Fitz Nowlan is the VP of AI and Architecture at SmartBear and the co-founder of Reflect, which is a web testing platform acquired by SmartBear in 2024. In this episode, Fitz joins Kevin Ball to discuss why web UI testing is uniquely challenging, how BearQ’s multi-agent architecture coordinates exploration and testing, why test data management becomes a hard distributed systems problem at scale, and what agentic development means for the future of QA. Full Disclosure: This episode is sponsored by SmartBear. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SmartBear and Multi-Agent QA appeared first on Software Engineering Daily.

AI coding tools have dramatically accelerated the pace of development, and the bottleneck in the software development lifecycle has shifted to code validation and testing. However, the conventional tools and workflows that QA teams have relied on were not designed for a world where a single engineer can generate thousands of lines of code in a day. SmartBear is a software quality platform spanning test automation, API lifecycle management, and observability. The company recently launched an AI-native QA platform called BearQ, which deploys autonomous agents that explore web applications, learns their structure and behavior, and authors and maintains test cases continuously. Fitz Nowlan is the VP of AI and Architecture at SmartBear and the co-founder of Reflect, which is a web testing platform acquired by SmartBear in 2024. In this episode, Fitz joins Kevin Ball to discuss why web UI testing is uniquely challenging, how BearQ’s multi-agent architecture coordinates exploration and testing, why test data management becomes a hard distributed systems problem at scale, and what agentic development means for the future of QA. Full Disclosure: This episode is sponsored by SmartBear. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SmartBear and Multi-Agent QA appeared first on Software Engineering Daily.

AI coding tools have dramatically accelerated the pace of development, and the bottleneck in the software development lifecycle has shifted to code validation and testing. However, the conventional tools and workflows that QA teams have relied on were not designed for a world where a single engineer can generate thousands of lines of code in a day. SmartBear is a software quality platform spanning test automation, API lifecycle management, and observability. The company recently launched an AI-native QA platform called BearQ, which deploys autonomous agents that explore web applications, learns their structure and behavior, and authors and maintains test cases continuously. Fitz Nowlan is the VP of AI and Architecture at SmartBear and the co-founder of Reflect, which is a web testing platform acquired by SmartBear in 2024. In this episode, Fitz joins Kevin Ball to discuss why web UI testing is uniquely challenging, how BearQ’s multi-agent architecture coordinates exploration and testing, why test data management becomes a hard distributed systems problem at scale, and what agentic development means for the future of QA. Full Disclosure: This episode is sponsored by SmartBear. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SmartBear and Multi-Agent QA appeared first on Software Engineering Daily.

AI agents are increasingly capable of reasoning and performing autonomous work over long periods. However, as agents take on more complex, longer-horizon tasks, keeping them supplied with the right information becomes the core engineering challenge. The industry is moving away from pre-loading context upfront toward a model where agents dynamically navigate and retrieve the data they need, when they need it. Redis is approaching context management using a context engine, which is an architecture built around four pillars: on-demand context retrieval, data that is always current, fast retrieval, and a memory layer that improves over time. In practice this means building materialized views of data with a semantic layer on top, rather than giving agents direct access to production databases. A memory system sits alongside this, extracting and compacting information asynchronously as the agent works. Simba Khadder leads AI strategy at Redis, and he previously co-founded the feature store platform FeatureForm, which was acquired by Redis in 2025. In this episode, Simba joins Kevin Ball to discuss why context has become the defining challenge in agentic AI, how context engines differ from traditional RAG architectures, how materialized views underpin reliable agent data pipelines, how memory systems can improve through async extraction and compaction, and how engineering teams need to adapt their practices as AI-driven development accelerates. Full Disclosure: This episode is sponsored by Redis. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Unlocking the Data Layer for Agentic AI with Simba Khadder appeared first on Software Engineering Daily.

AI agents are increasingly capable of reasoning and performing autonomous work over long periods. However, as agents take on more complex, longer-horizon tasks, keeping them supplied with the right information becomes the core engineering challenge. The industry is moving away from pre-loading context upfront toward a model where agents dynamically navigate and retrieve the data they need, when they need it. Redis is approaching context management using a context engine, which is an architecture built around four pillars: on-demand context retrieval, data that is always current, fast retrieval, and a memory layer that improves over time. In practice this means building materialized views of data with a semantic layer on top, rather than giving agents direct access to production databases. A memory system sits alongside this, extracting and compacting information asynchronously as the agent works. Simba Khadder leads AI strategy at Redis, and he previously co-founded the feature store platform FeatureForm, which was acquired by Redis in 2025. In this episode, Simba joins Kevin Ball to discuss why context has become the defining challenge in agentic AI, how context engines differ from traditional RAG architectures, how materialized views underpin reliable agent data pipelines, how memory systems can improve through async extraction and compaction, and how engineering teams need to adapt their practices as AI-driven development accelerates. Full Disclosure: This episode is sponsored by Redis. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Unlocking the Data Layer for Agentic AI with Simba Khadder appeared first on Software Engineering Daily.

AI agents are increasingly capable of reasoning and performing autonomous work over long periods. However, as agents take on more complex, longer-horizon tasks, keeping them supplied with the right information becomes the core engineering challenge. The industry is moving away from pre-loading context upfront toward a model where agents dynamically navigate and retrieve the data they need, when they need it. Redis is approaching context management using a context engine, which is an architecture built around four pillars: on-demand context retrieval, data that is always current, fast retrieval, and a memory layer that improves over time. In practice this means building materialized views of data with a semantic layer on top, rather than giving agents direct access to production databases. A memory system sits alongside this, extracting and compacting information asynchronously as the agent works. Simba Khadder leads AI strategy at Redis, and he previously co-founded the feature store platform FeatureForm, which was acquired by Redis in 2025. In this episode, Simba joins Kevin Ball to discuss why context has become the defining challenge in agentic AI, how context engines differ from traditional RAG architectures, how materialized views underpin reliable agent data pipelines, how memory systems can improve through async extraction and compaction, and how engineering teams need to adapt their practices as AI-driven development accelerates. Full Disclosure: This episode is sponsored by Redis. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Unlocking the Data Layer for Agentic AI with Simba Khadder appeared first on Software Engineering Daily.

Interactive notebooks were popularized by the Jupyter project and have since become a core tool for data science, research, and data exploration. However, traditional, imperative notebooks often break down as projects grow more complex. Hidden state, non-reproducible execution, poor version control ergonomics, and difficulty reusing notebook code in real software systems make it hard to move from exploration to production. At the same time, sharing results often requires collaborators to recreate entire environments, limiting interactivity and slowing feedback. Marimo is an open-source, next-generation Python notebook designed to address these problems directly. Akshay Agrawal is the creator of Marimo and he previously worked at Google Brain. He joins the show with Kevin Ball to discuss the limitations of traditional notebooks, the design of reactive notebooks in Python, how marimo bridges research and production, and where notebooks fit in an increasingly agentic, AI-assisted development world. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Reinventing the Python Notebook with Akshay Agrawal appeared first on Software Engineering Daily.

Interactive notebooks were popularized by the Jupyter project and have since become a core tool for data science, research, and data exploration. However, traditional, imperative notebooks often break down as projects grow more complex. Hidden state, non-reproducible execution, poor version control ergonomics, and difficulty reusing notebook code in real software systems make it hard to move from exploration to production. At the same time, sharing results often requires collaborators to recreate entire environments, limiting interactivity and slowing feedback. Marimo is an open-source, next-generation Python notebook designed to address these problems directly. Akshay Agrawal is the creator of Marimo and he previously worked at Google Brain. He joins the show with Kevin Ball to discuss the limitations of traditional notebooks, the design of reactive notebooks in Python, how marimo bridges research and production, and where notebooks fit in an increasingly agentic, AI-assisted development world. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Reinventing the Python Notebook with Akshay Agrawal appeared first on Software Engineering Daily.

Interactive notebooks were popularized by the Jupyter project and have since become a core tool for data science, research, and data exploration. However, traditional, imperative notebooks often break down as projects grow more complex. Hidden state, non-reproducible execution, poor version control ergonomics, and difficulty reusing notebook code in real software systems make it hard to move from exploration to production. At the same time, sharing results often requires collaborators to recreate entire environments, limiting interactivity and slowing feedback. Marimo is an open-source, next-generation Python notebook designed to address these problems directly. Akshay Agrawal is the creator of Marimo and he previously worked at Google Brain. He joins the show with Kevin Ball to discuss the limitations of traditional notebooks, the design of reactive notebooks in Python, how marimo bridges research and production, and where notebooks fit in an increasingly agentic, AI-assisted development world. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Reinventing the Python Notebook with Akshay Agrawal appeared first on Software Engineering Daily.

AI agents have taken on a growing share of software development work, so much so that the hardest problems are shifting away from code generation towards something new, context. The challenge is now contextualizing why systems work the way they do, how architectural decisions were made, and the sources of truth that exist outside of the code base. As teams adopt agentic tools, gaps or inconsistencies in context have emerged as a primary reason why software fails to meet production standards. Unblocked is a startup focused on solving this context gap. Their context engine aggregates and reasons over organizational knowledge spread across source code, pull requests, documentation, chat systems, and production telemetry. By acting as a context engine for both developers and AI agents, Unblocked aims to improve AI code quality and review, reduce interruptions, accelerate onboarding, and enable safer, more effective agentic workflows. Dennis Pilarinos is the Founder and CEO of Unblocked. Previously, he helped build Azure at Microsoft, worked at AWS, and co-founded BuddyBuild, which is a mobile CI platform acquired by Apple. Dennis joins Kevin Ball to discuss context engineering, reconciling conflicting sources nof truth, permission to wear AI systems, the shifting bottlenecks in the software development lifecycle, and what it means to be a software engineer in an increasingly agentic world. Full Disclosure: This episode is sponsored by Unblocked. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Organizational Context for AI Coding Agents with Dennis Pilarinos appeared first on Software Engineering Daily.

AI agents have taken on a growing share of software development work, so much so that the hardest problems are shifting away from code generation towards something new, context. The challenge is now contextualizing why systems work the way they do, how architectural decisions were made, and the sources of truth that exist outside of the code base. As teams adopt agentic tools, gaps or inconsistencies in context have emerged as a primary reason why software fails to meet production standards. Unblocked is a startup focused on solving this context gap. Their context engine aggregates and reasons over organizational knowledge spread across source code, pull requests, documentation, chat systems, and production telemetry. By acting as a context engine for both developers and AI agents, Unblocked aims to improve AI code quality and review, reduce interruptions, accelerate onboarding, and enable safer, more effective agentic workflows. Dennis Pilarinos is the Founder and CEO of Unblocked. Previously, he helped build Azure at Microsoft, worked at AWS, and co-founded BuddyBuild, which is a mobile CI platform acquired by Apple. Dennis joins Kevin Ball to discuss context engineering, reconciling conflicting sources nof truth, permission to wear AI systems, the shifting bottlenecks in the software development lifecycle, and what it means to be a software engineer in an increasingly agentic world. Full Disclosure: This episode is sponsored by Unblocked. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Organizational Context for AI Coding Agents with Dennis Pilarinos appeared first on Software Engineering Daily.

AI agents have taken on a growing share of software development work, so much so that the hardest problems are shifting away from code generation towards something new, context. The challenge is now contextualizing why systems work the way they do, how architectural decisions were made, and the sources of truth that exist outside of the code base. As teams adopt agentic tools, gaps or inconsistencies in context have emerged as a primary reason why software fails to meet production standards. Unblocked is a startup focused on solving this context gap. Their context engine aggregates and reasons over organizational knowledge spread across source code, pull requests, documentation, chat systems, and production telemetry. By acting as a context engine for both developers and AI agents, Unblocked aims to improve AI code quality and review, reduce interruptions, accelerate onboarding, and enable safer, more effective agentic workflows. Dennis Pilarinos is the Founder and CEO of Unblocked. Previously, he helped build Azure at Microsoft, worked at AWS, and co-founded BuddyBuild, which is a mobile CI platform acquired by Apple. Dennis joins Kevin Ball to discuss context engineering, reconciling conflicting sources nof truth, permission to wear AI systems, the shifting bottlenecks in the software development lifecycle, and what it means to be a software engineer in an increasingly agentic world. Full Disclosure: This episode is sponsored by Unblocked. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Organizational Context for AI Coding Agents with Dennis Pilarinos appeared first on Software Engineering Daily.

AI-assisted coding tools have made it easier than ever to spin up prototypes, but turning those prototypes into reliable, production-grade systems remains a major challenge. Large language models are non-deterministic, prone to drift, and often lose track of intent over long development sessions. Kiro is an AI-powered IDE that’s built around a spec-driven development workflow. It’s focused on helping developers capture intent up front, translate it into concrete requirements and designs, and systematically validate implementations through tasks, testing, and guardrails. It aims to preserve the creativity of AI-assisted development while producing software that is ready for real-world use. David Yanacek is a Senior Principal Engineer and a lead advisor on the Agentic AI team at AWS. Today, his work focuses on Kiro, frontier agents, Amazon Bedrock AgentCore, and AWS's operational agents. He joins the show with Kevin Ball to discuss the design of Kiro, how spec-driven development changes the way teams work with AI coding agents, and what the next generation of agentic software development might look like. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Amazon's IDE for Spec-Driven Development with David Yanacek appeared first on Software Engineering Daily.

AI-assisted coding tools have made it easier than ever to spin up prototypes, but turning those prototypes into reliable, production-grade systems remains a major challenge. Large language models are non-deterministic, prone to drift, and often lose track of intent over long development sessions. Kiro is an AI-powered IDE that’s built around a spec-driven development workflow. It’s focused on helping developers capture intent up front, translate it into concrete requirements and designs, and systematically validate implementations through tasks, testing, and guardrails. It aims to preserve the creativity of AI-assisted development while producing software that is ready for real-world use. David Yanacek is a Senior Principal Engineer and a lead advisor on the Agentic AI team at AWS. Today, his work focuses on Kiro, frontier agents, Amazon Bedrock AgentCore, and AWS's operational agents. He joins the show with Kevin Ball to discuss the design of Kiro, how spec-driven development changes the way teams work with AI coding agents, and what the next generation of agentic software development might look like. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Amazon's IDE for Spec-Driven Development with David Yanacek appeared first on Software Engineering Daily.

AI-assisted coding tools have made it easier than ever to spin up prototypes, but turning those prototypes into reliable, production-grade systems remains a major challenge. Large language models are non-deterministic, prone to drift, and often lose track of intent over long development sessions. Kiro is an AI-powered IDE that’s built around a spec-driven development workflow. It’s focused on helping developers capture intent up front, translate it into concrete requirements and designs, and systematically validate implementations through tasks, testing, and guardrails. It aims to preserve the creativity of AI-assisted development while producing software that is ready for real-world use. David Yanacek is a Senior Principal Engineer and a lead advisor on the Agentic AI team at AWS. Today, his work focuses on Kiro, frontier agents, Amazon Bedrock AgentCore, and AWS's operational agents. He joins the show with Kevin Ball to discuss the design of Kiro, how spec-driven development changes the way teams work with AI coding agents, and what the next generation of agentic software development might look like. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Amazon's IDE for Spec-Driven Development with David Yanacek appeared first on Software Engineering Daily.

LLM -powered systems continue to move steadily into production, but this process is presenting teams with challenges that traditional software practices don’t commonly encounter. Models and agents are non-deterministic systems, which makes it difficult to test changes, reason about failures, and confidently ship updates. This has created the need for new evaluation tooling designed specifically around the properties of LLMs. Comet is a platform with Roots and MLOps, to the rapidly evolving world of agent-based systems by treating prompts, tools, and workflows as optimizable components that can be evaluated and improved over time. Gideon Mendels is the co -founder and CEO of Comet. He previously worked at Google on hate speech and deception detection, and he founded GroupWise, which trained and deployed NLP models processing billions of chats. In this episode, Gideon joins Kevin Ball to discuss how agent development sits between software engineering and ML, why eVals are the missing foundation for most AI teams, prompt optimization as a search problem, and the future for continuously improving agents in production. Full Disclosure: This episode is sponsored by Comet. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Optimizing Agent Behavior in Production with Gideon Mendels appeared first on Software Engineering Daily.

LLM -powered systems continue to move steadily into production, but this process is presenting teams with challenges that traditional software practices don’t commonly encounter. Models and agents are non-deterministic systems, which makes it difficult to test changes, reason about failures, and confidently ship updates. This has created the need for new evaluation tooling designed specifically around the properties of LLMs. Comet is a platform with Roots and MLOps, to the rapidly evolving world of agent-based systems by treating prompts, tools, and workflows as optimizable components that can be evaluated and improved over time. Gideon Mendels is the co -founder and CEO of Comet. He previously worked at Google on hate speech and deception detection, and he founded GroupWise, which trained and deployed NLP models processing billions of chats. In this episode, Gideon joins Kevin Ball to discuss how agent development sits between software engineering and ML, why eVals are the missing foundation for most AI teams, prompt optimization as a search problem, and the future for continuously improving agents in production. Full Disclosure: This episode is sponsored by Comet. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Optimizing Agent Behavior in Production with Gideon Mendels appeared first on Software Engineering Daily.

LLM -powered systems continue to move steadily into production, but this process is presenting teams with challenges that traditional software practices don’t commonly encounter. Models and agents are non-deterministic systems, which makes it difficult to test changes, reason about failures, and confidently ship updates. This has created the need for new evaluation tooling designed specifically around the properties of LLMs. Comet is a platform with Roots and MLOps, to the rapidly evolving world of agent-based systems by treating prompts, tools, and workflows as optimizable components that can be evaluated and improved over time. Gideon Mendels is the co -founder and CEO of Comet. He previously worked at Google on hate speech and deception detection, and he founded GroupWise, which trained and deployed NLP models processing billions of chats. In this episode, Gideon joins Kevin Ball to discuss how agent development sits between software engineering and ML, why eVals are the missing foundation for most AI teams, prompt optimization as a search problem, and the future for continuously improving agents in production. Full Disclosure: This episode is sponsored by Comet. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Optimizing Agent Behavior in Production with Gideon Mendels appeared first on Software Engineering Daily.

AI-assisted programming has moved far beyond autocomplete. Large language models are now capable of editing entire codebases, coordinating long-running tasks, and collaborating across multiple systems. As these capabilities mature, the core challenge in software development is shifting away from writing code and toward orchestrating work, managing context, and maintaining shared understanding across fleets of agents. Steve Yegge is a software engineer, writer, and industry veteran whose essays have shaped how many developers think about their work. Over the past year, Steve has been exploring the frontier of agentic software development, building tools like Beads and Gas Town to experiment with multi-agent coordination, shared memory, and AI-driven software workflows. In this episode, Steve joins Kevin Ball to discuss the evolution of AI coding from chat-based assistance to full agent orchestration, the technical and cognitive challenges of managing fleets of agents, how concepts like task graphs and Git-backed ledgers change the nature of work, and what these shifts mean for software teams, tooling, and the future of the industry. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Gas Town, Beads, and the Rise of Agentic Development with Steve Yegge appeared first on Software Engineering Daily.

AI-assisted programming has moved far beyond autocomplete. Large language models are now capable of editing entire codebases, coordinating long-running tasks, and collaborating across multiple systems. As these capabilities mature, the core challenge in software development is shifting away from writing code and toward orchestrating work, managing context, and maintaining shared understanding across fleets of agents. Steve Yegge is a software engineer, writer, and industry veteran whose essays have shaped how many developers think about their work. Over the past year, Steve has been exploring the frontier of agentic software development, building tools like Beads and Gas Town to experiment with multi-agent coordination, shared memory, and AI-driven software workflows. In this episode, Steve joins Kevin Ball to discuss the evolution of AI coding from chat-based assistance to full agent orchestration, the technical and cognitive challenges of managing fleets of agents, how concepts like task graphs and Git-backed ledgers change the nature of work, and what these shifts mean for software teams, tooling, and the future of the industry. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Gas Town, Beads, and the Rise of Agentic Development with Steve Yegge appeared first on Software Engineering Daily.

AI-assisted programming has moved far beyond autocomplete. Large language models are now capable of editing entire codebases, coordinating long-running tasks, and collaborating across multiple systems. As these capabilities mature, the core challenge in software development is shifting away from writing code and toward orchestrating work, managing context, and maintaining shared understanding across fleets of agents. Steve Yegge is a software engineer, writer, and industry veteran whose essays have shaped how many developers think about their work. Over the past year, Steve has been exploring the frontier of agentic software development, building tools like Beads and Gas Town to experiment with multi-agent coordination, shared memory, and AI-driven software workflows. In this episode, Steve joins Kevin Ball to discuss the evolution of AI coding from chat-based assistance to full agent orchestration, the technical and cognitive challenges of managing fleets of agents, how concepts like task graphs and Git-backed ledgers change the nature of work, and what these shifts mean for software teams, tooling, and the future of the industry. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Gas Town, Beads, and the Rise of Agentic Development with Steve Yegge appeared first on Software Engineering Daily.

AI coding agents are rapidly reshaping how software is built, reviewed, and maintained. As large language model capabilities continue to increase, the bottleneck in software development is shifting away from code generation toward planning, review, deployment, and coordination. This shift is driving a new class of agentic systems that operate inside constrained environments, reason over long time horizons, and integrate across tools like IDEs, version control systems, and issue trackers. OpenAI is at the forefront of AI research and product development. In 2025, the company released Codex, which is an agentic coding system designed to work safely inside sandboxed environments while collaborating across the modern software development stack. Thibault Sottiaux is the Codex engineering lead and Ed Bayes is the Codex product designer. In this episode, they join Kevin Ball to discuss how Codex is built, the co-evolution of models and harnesses, multi-agent futures, Codex's open-source CLI, model specialization, latency and performance considerations, and much more. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post OpenAI and Codex with Thibault Sottiaux and Ed Bayes appeared first on Software Engineering Daily.

AI coding agents are rapidly reshaping how software is built, reviewed, and maintained. As large language model capabilities continue to increase, the bottleneck in software development is shifting away from code generation toward planning, review, deployment, and coordination. This shift is driving a new class of agentic systems that operate inside constrained environments, reason over long time horizons, and integrate across tools like IDEs, version control systems, and issue trackers. OpenAI is at the forefront of AI research and product development. In 2025, the company released Codex, which is an agentic coding system designed to work safely inside sandboxed environments while collaborating across the modern software development stack. Thibault Sottiaux is the Codex engineering lead and Ed Bayes is the Codex product designer. In this episode, they join Kevin Ball to discuss how Codex is built, the co-evolution of models and harnesses, multi-agent futures, Codex's open-source CLI, model specialization, latency and performance considerations, and much more. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post OpenAI and Codex with Thibault Sottiaux and Ed Bayes appeared first on Software Engineering Daily.

AI coding agents are rapidly reshaping how software is built, reviewed, and maintained. As large language model capabilities continue to increase, the bottleneck in software development is shifting away from code generation toward planning, review, deployment, and coordination. This shift is driving a new class of agentic systems that operate inside constrained environments, reason over long time horizons, and integrate across tools like IDEs, version control systems, and issue trackers. OpenAI is at the forefront of AI research and product development. In 2025, the company released Codex, which is an agentic coding system designed to work safely inside sandboxed environments while collaborating across the modern software development stack. Thibault Sottiaux is the Codex engineering lead and Ed Bayes is the Codex product designer. In this episode, they join Kevin Ball to discuss how Codex is built, the co-evolution of models and harnesses, multi-agent futures, Codex's open-source CLI, model specialization, latency and performance considerations, and much more. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post OpenAI and Codex with Thibault Sottiaux and Ed Bayes appeared first on Software Engineering Daily.

Engineering teams around the world are building AI-focused applications or integrating AI features into existing products. The AI development ecosystem is maturing, which is accelerating how quickly these applications can be prototyped. However, taking AI applications to production remains a notoriously complex process. Modern AI stacks demand LLMs, embeddings, vector search, observability, new caching layers, and constant adaptation as the landscape shifts week to week. Increasingly, the data layer has become both the foundation and the bottleneck to AI app productionization. MongoDB has been expanding beyond its core document database into a full AI-ready database platform with integrated capabilities for operational data, search, real-time analytics, and AI-powered data retrieval. The company also recently acquired Voyage AI to provide accurate and cost-effective embedding models and rerankers to its users. Fred Roma is a veteran engineer and is currently the SVP of Product and Engineering at MongoDB. He joins the show with Kevin Ball to talk about the state of AI application development, the role of vector search and reranking, schema evolution in the LLM era, the Voyage AI acquisition, how data platforms must evolve to keep up with AI's breakneck pace, and more. Full Disclosure: This episode is sponsored by MongoDB. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Production-Grade AI Systems with Fred Roma appeared first on Software Engineering Daily.

Engineering teams around the world are building AI-focused applications or integrating AI features into existing products. The AI development ecosystem is maturing, which is accelerating how quickly these applications can be prototyped. However, taking AI applications to production remains a notoriously complex process. Modern AI stacks demand LLMs, embeddings, vector search, observability, new caching layers, and constant adaptation as the landscape shifts week to week. Increasingly, the data layer has become both the foundation and the bottleneck to AI app productionization. MongoDB has been expanding beyond its core document database into a full AI-ready database platform with integrated capabilities for operational data, search, real-time analytics, and AI-powered data retrieval. The company also recently acquired Voyage AI to provide accurate and cost-effective embedding models and rerankers to its users. Fred Roma is a veteran engineer and is currently the SVP of Product and Engineering at MongoDB. He joins the show with Kevin Ball to talk about the state of AI application development, the role of vector search and reranking, schema evolution in the LLM era, the Voyage AI acquisition, how data platforms must evolve to keep up with AI's breakneck pace, and more. Full Disclosure: This episode is sponsored by MongoDB. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Production-Grade AI Systems with Fred Roma appeared first on Software Engineering Daily.

Engineering teams around the world are building AI-focused applications or integrating AI features into existing products. The AI development ecosystem is maturing, which is accelerating how quickly these applications can be prototyped. However, taking AI applications to production remains a notoriously complex process. Modern AI stacks demand LLMs, embeddings, vector search, observability, new caching layers, and constant adaptation as the landscape shifts week to week. Increasingly, the data layer has become both the foundation and the bottleneck to AI app productionization. MongoDB has been expanding beyond its core document database into a full AI-ready database platform with integrated capabilities for operational data, search, real-time analytics, and AI-powered data retrieval. The company also recently acquired Voyage AI to provide accurate and cost-effective embedding models and rerankers to its users. Fred Roma is a veteran engineer and is currently the SVP of Product and Engineering at MongoDB. He joins the show with Kevin Ball to talk about the state of AI application development, the role of vector search and reranking, schema evolution in the LLM era, the Voyage AI acquisition, how data platforms must evolve to keep up with AI's breakneck pace, and more. Full Disclosure: This episode is sponsored by MongoDB. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Production-Grade AI Systems with Fred Roma appeared first on Software Engineering Daily.

WebAssembly, or WASM, has grown from a low-level compilation target for C and C++ into one of the most influential technologies in modern computing. It now powers browser applications, edge compute platforms, embedded systems, and a growing ecosystem of languages targeting a portable and secure execution model. Andreas Rossberg is a programming languages researcher and former member of the V8 team at Google. Andreas helped architect WebAssembly from its earliest concepts through its most recent milestone releases, including the groundbreaking 3.0 spec that introduces garbage collection, richer reference types, and major steps toward multi-language interoperability. In this episode, Andreas joins Kevin Ball to explore the history of WebAssembly, the constraints that shaped its earliest design, the major turning points in versions 1.0, 2.0, and 3.0, and what's coming next for WebAssembly. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post WebAssembly 3.0 with Andreas Rossberg appeared first on Software Engineering Daily.

WebAssembly, or WASM, has grown from a low-level compilation target for C and C++ into one of the most influential technologies in modern computing. It now powers browser applications, edge compute platforms, embedded systems, and a growing ecosystem of languages targeting a portable and secure execution model. Andreas Rossberg is a programming languages researcher and former member of the V8 team at Google. Andreas helped architect WebAssembly from its earliest concepts through its most recent milestone releases, including the groundbreaking 3.0 spec that introduces garbage collection, richer reference types, and major steps toward multi-language interoperability. In this episode, Andreas joins Kevin Ball to explore the history of WebAssembly, the constraints that shaped its earliest design, the major turning points in versions 1.0, 2.0, and 3.0, and what's coming next for WebAssembly. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post WebAssembly 3.0 with Andreas Rossberg appeared first on Software Engineering Daily.

WebAssembly, or WASM, has grown from a low-level compilation target for C and C++ into one of the most influential technologies in modern computing. It now powers browser applications, edge compute platforms, embedded systems, and a growing ecosystem of languages targeting a portable and secure execution model. Andreas Rossberg is a programming languages researcher and former member of the V8 team at Google. Andreas helped architect WebAssembly from its earliest concepts through its most recent milestone releases, including the groundbreaking 3.0 spec that introduces garbage collection, richer reference types, and major steps toward multi-language interoperability. In this episode, Andreas joins Kevin Ball to explore the history of WebAssembly, the constraints that shaped its earliest design, the major turning points in versions 1.0, 2.0, and 3.0, and what's coming next for WebAssembly. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post WebAssembly 3.0 with Andreas Rossberg appeared first on Software Engineering Daily.

Surveillance technology is advancing faster than the laws meant to govern it. Across the United States, police departments are deploying automated license plate readers, facial recognition tools, and predictive systems that quietly log the daily movements of millions of people. These tools promise efficiency and safety, but critics argue that they represent a form of warrantless mass surveillance, and raise deep constitutional questions about privacy, accountability, and the limits of government power in the digital age. Michael Soyfer is an attorney at the Institute for Justice, a nonprofit public interest law firm focused on defending individual rights. His work centers on the Fourth Amendment and the growing use of surveillance technologies by local governments. Michael joins the show with Kevin Ball to discuss the rise of Flock Safety cameras, the Institute for Justice's lawsuit against the City of Norfolk, how decades-old legal precedents struggle to keep up with modern technology, and what citizens, technologists, and policymakers can do to protect privacy in an era of pervasive data collection. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post America Under Surveillance with Michael Soyfer appeared first on Software Engineering Daily.

Modern software development is more complex than ever. Teams work across different operating systems, chip architectures, and cloud environments, each with its own dependency quirks and version mismatches. Ensuring that code runs reproducibly across these environments has become a major challenge that's made even harder by growing concerns around software supply chain security. Nix is a powerful open-source package manager that builds software in controlled, declarative environments where dependencies are explicitly defined and reproducible. Its functional approach has made it a gold standard for reproducible builds, but it can also be difficult to learn and adopt. Flox is a company that builds on top of Nix, with increased supply chain security and abstractions that streamline the developer experience. Michael Stahnke is the VP of Engineering at Flox and formerly worked at companies including Caterpillar, Puppet, and CircleCI. He joins the podcast with Kevin Ball to talk about Flox, building on top of Nix, how reproducibility underpins software security, the concept of “secure by construction, how deterministic environments are reshaping both human and AI-driven development, and much more. Full Disclosure: This episode is sponsored by Flox. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Flox, Nix, and Reproducible Software Systems with Michael Stahnke appeared first on Software Engineering Daily.

Visual Studio Code has become one of the most influential tools in modern software development. The open-source code editor has evolved into a platform used by millions of developers around the world, and it has reshaped expectations for what a modern development environment can be through its intuitive UX, rich extension marketplace, and deep integration with today's tooling landscape. Now, in an era defined by rapid advances in AI-assisted programming, VS Code is at the center of a profound shift in how software is written. Kai Maetzel is the Engineering Manager leading the VS Code team at Microsoft. He joins the show with Kevin Ball to talk about the origins of VS Code, how AI has reshaped the editor's design philosophy, the rise of agentic programming models, and what the future of development might look like. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post VS Code and Agentic Development with Kai Maetzel appeared first on Software Engineering Daily.