Podcast
Before The Commit
Hosted by Danny Gershman, Dustin Hilgaertner · EN
AI is writing your code. Who's watching the AI? Before The Commit explores AI coding security, emerging threats, and the trends reshaping software development. Hosts Danny Gershman and Dustin Hilgaertner break down threat models, prompt injection, shadow AI, and practical defenses — drawing from experience across defense, fintech, and enterprise environments. Companion to the book Before The Commit: Securing AI in the Age of Autonomous Code. No hype, just tactical insight for developers, security engineers, and leaders building in the AI era.
17episodes
Episodes
Newest firstAll episodes
Episode 32: Cursor
1w ago01:06:58Tap to summarizeThe conversation covers the introduction to Cursor, the transition to Riverside, experimenting with Cursor and Crow, SpaceX's acquisition of Cursor, Cursor's evolution and future predictions, features of Cursor and comparison with other tools, Nvidia's RTX Spark, and a discussion on AI usage and Apple's AI performance. The conversation covers a range of topics including Apple's AI competition, Siri 2 and Gemini integration, challenges with AI assistants, GitHub's Co-Pilot billing shift, the AI coding arms race, recent AI model releases, new AI tools and models, Grok subscription and plateauing, Claude Code's workflows feature, understanding workflows and goals, US government's stake in OpenAI, implications of government involvement, executive orders and AI regulation, and Anthropic's position and government relations.TakeawaysCursor's evolution and future predictionsNvidia's RTX Spark and its impact on AI usage Competition in the AI space is intensifying, with new releases and features from major players.Government involvement in AI regulation and oversight is a growing concern.Chapters00:00 Introduction to Cursor and News08:20 Experimenting with Cursor and Crow16:27 Cursor's Evolution and Future Predictions26:18 Nvidia's RTX Spark and Apple's AI Platform37:14 Apple's AI Competition43:12 Grok Build and Composer Integration52:21 Implications of Government Involvement57:23 Executive Orders and AI Regulation01:05:04 Government's Oversight of AI Models
Transcribe →Episode 31: Sam Kassoumeh, Co-Founder @ SecurityScorecard
3w ago01:04:53Tap to summarizeThe conversation covers the topics of AI security gateways, SaaS-based companies, AI in coding, the evolution of Security Scorecard, and the impact of AI on threat intelligence data. The conversation delves into the transformative impact of AI and Threat Intel on data analysis, product development, and organizational workflows. It explores the exponential growth in interconnectivity and observation data, the value of net flow data when run through models, and the automation of manual tasks in identifying and cross-correlating data sets. The intersection of AI and Threat Intel is redefining the assessment process, transforming workflows, and changing the roles and responsibilities within organizations.TakeawaysAI security gateways are a hot commodity in the security space.SaaS companies are doing more with less, leveraging AI and automation.AI is changing the way coding is done, reducing the need for human intervention.Security Scorecard was founded to address the growing dependency on supply chain partners and third parties.AI has revolutionized threat intelligence data, uncovering deeper insights and network connections. Exponential growth in interconnectivity and observation dataValue of net flow data when run through modelsRedefining the assessment process and transforming workflowsChapters00:00 AI Security Gateways in the Security Space07:35 AI's Impact on Coding and Automation28:44 AI's Impact on Threat Intelligence Data34:31 Value of Net Flow Data When Run Through Models
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Episode 30: Steve Thomas, CEO @ HackNotice
May 1200:59:51Tap to summarizeThe podcast episode features Steve Thomas, CEO of Hack Notice, discussing the evolving landscape of AI in cybersecurity and its broader impact.**AI's Disruption and Industry Parallels:**\The conversation begins by drawing parallels between AI's impact and the software engineering industry. Just as AI didn't replace engineers but amplified their productivity, it's expected to do the same in other sectors. The legal industry, for instance, is seeing disruption from AI tools like Claude, potentially lowering costs and increasing accessibility. This shift is moving pricing from billable hours to value-based models, a trend observed in SaaS development where traditional seat-based pricing is becoming obsolete with the rise of AI agents.**Threat Intelligence and Hack Notice's Approach:**\Steve Thomas shares his experience founding Pwnlist, a pioneer in breach monitoring, highlighting his focus on addressing "hated problems" in cybersecurity. Hack Notice, his current venture, tackles third-party risk by applying a threat-informed approach, analyzing data from hacker forums and credential leaks to provide an adversary's perspective on vendor vulnerabilities. This contrasts with traditional cyber hygiene metrics, focusing instead on the operational tactics of threat actors.**AI's Role in Cybercrime:**\The discussion delves into how AI is accelerating cyber threats. AI is enabling more sophisticated and personalized attacks, such as spear-phishing, and potentially improving vulnerability discovery. The rise of info-stealer malware, which rapidly harvests credentials, API keys, and browser data, is a significant concern. This malware's efficiency and the increasing volume of stolen data, particularly AI-related credentials, pose a substantial risk.**Security Implications and CISOs' Concerns:**\While CISOs are aware of AI's dual role as both a tool for defense and a weapon for attackers, their immediate focus is often on leveraging AI for efficiency and productivity rather than solely on its security risks. The rapid proliferation of AI tools, including potentially insecure open-source models and cloud-based services, creates a challenging environment. The lack of robust data governance for AI usage by employees is a significant concern, as this data can reveal intent and potentially lead to legal or security breaches.**The Future of AI in Cybersecurity:**\Steve emphasizes that the AI landscape is volatile, with rapid advancements and market shifts. He believes that companies with deep domain expertise and a focus on building reliable, guard-railed AI solutions will succeed. The traditional models of threat intelligence reporting are becoming obsolete, needing to be machine- and AI-readable. He advises CISOs to focus on the basics of cybersecurity, understand their adversaries, and carefully manage AI integration, particularly by avoiding direct access to production environments for AI systems. The key takeaway is that AI is an accelerator, making both offensive and defensive capabilities more potent, and thus underscoring the need for robust, AI-informed security strategies.
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Episode 29: Agentgateway and Portkey
Apr 2300:59:40Tap to summarizeHere's a summary of the video transcript:The podcast episode covers several key topics related to AI and technology.**SpaceX Acquires Cursor:** A significant portion of the discussion revolves around SpaceX's potential acquisition of Cursor, an AI-powered code editor. The deal is valued at $60 billion, highlighting the increasing value placed on AI and software development tools. The merger of XAI (Elon Musk's AI company) into SpaceX is explained as the entity behind this acquisition. This move is seen as SpaceX's strategy to bolster its AI capabilities, particularly in coding, by acquiring Cursor's technology and talent. The acquisition is also discussed in the context of existing AI coding tools like Claude Code and OpenAI's Codex.**The Value of Software and Talent:** The high valuation of Cursor, a company that emerged recently, underscores the immense value of software and the engineering talent behind it. The discussion touches on the idea of "acqui-hiring," where companies acquire others primarily for their skilled workforce. The $60 billion figure is considered substantial, even for an "aqua hire," emphasizing the scarcity and importance of specialized AI and software engineering talent.**AI Gateways: Portkey and Agent Gateway:** The "Tool of the Week" segment delves into AI gateways.- **Agent Gateway (Solo AI):** This solution is described as a Kubernetes-based orchestration tool for managing AI agents. It focuses on providing governance, policies, and routing rules for containerized AI agents within a Kubernetes cluster, integrating with tools like Istio. It's positioned as an "AI governance" solution for managing inter-agent communication.- **Portkey:** This is presented as a SaaS-based AI gateway that acts as a proxy server. It offers features like user management, analytics, logging, and a robust system for managing API keys, prompts, and guardrails. A unique aspect highlighted is Portkey's ability to manage prompts and their versioning outside of application code, enabling A/B testing and easier modification of AI behavior without code changes. It also supports agent integration via the A2A protocol.**AI's Impact on the Workforce and Layoffs:** The podcast discusses the broader implications of AI on employment. Snap's recent layoff of 1,000 employees is cited, with the CEO attributing it to AI taking over a significant portion of coding tasks (over 65%). This sparks a discussion on whether these layoffs are due to overhiring or a genuine shift in required skills, suggesting that companies are adapting to AI's capabilities by seeking new types of talent or upskilling existing employees. The trend is seen as a leading indicator for other industries, implying a future where AI augmentation or replacement of roles will become more common across various departments, not just engineering.**AI and Copyright Concerns:** A significant legal development is discussed: Anthropic's argument before a federal judge that training its AI models on copyrighted song lyrics constitutes "transformative fair use." This case is seen as setting important legal precedents for the entire AI industry regarding the use of copyrighted data for training. The discussion touches on the vast scale of data used in AI training, the immense potential copyright infringement damages, and the practical challenges of enforcing these laws in the AI era. The analogy is made between how humans learn from creative works and how AI models are trained, raising questions about the future of intellectual property in the age of AI.
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Episode 28: Cloudflare AI Gateway
Apr 1501:04:02Tap to summarizeThe video discusses several key topics related to AI and its impact on the tech industry.Firstly, it delves into Anthropic's "Mythos" model and "Project Glasswing." The speaker expresses skepticism about the hyped claims surrounding Mythos, suggesting that the limited release might be due to resource constraints (GPU availability) rather than its groundbreaking capabilities. The speaker draws parallels to Anthropic's past PR strategies, citing the "blackmailed engineer" story as an example of manufactured hype.Secondly, the video addresses the perceived "nerfing" of Anthropic's Claude Code. The speaker details a series of changes, including the introduction of "adaptive thinking," a reduction in default "effort" settings from high to medium, and the removal of visible "thinking" logs from the UI. These changes, while potentially offering cost savings for Anthropic, have led to performance degradation for users, particularly those engaging in complex tasks. The speaker notes that while these changes can be reverted manually, the opt-out nature and the timing of these updates are concerning.Thirdly, the discussion shifts to Cloudflare's AI Gateway. The speaker highlights its features, including virtual gateways with unique hashes for custom rules, compatibility with various SDKs (OpenAI, Anthropic), and logging capabilities. A key aspect is Cloudflare's use of Llama for processing "guardrails," which are implemented for content moderation (e.g., blocking defamation or political content). The speaker also notes the limitations of these guardrails, such as the lack of regex support for sensitive data like API keys, suggesting the gateway is more suited for corporate chatbots than coding environments. The caching, rate limiting, and alias features for API keys are also discussed as beneficial for managing AI access.Finally, the video touches upon the impact of AI on junior engineers. Statistics are presented indicating a decline in "programmer" job postings, contrasting with a smaller drop in "software developer" roles. The speaker suggests a shift from task-based junior roles to more AI-centric orchestration of agents. The speaker predicts a future shortage of software engineers, with companies increasingly needing junior engineers to manage AI systems, thereby elevating the importance of mentorship in AI agent management. The video concludes with a broader discussion on how AI is transforming various careers and the need for educational institutions to adapt their curricula to include AI proficiency. The overall sentiment is that while AI adoption presents challenges, it also creates significant opportunities for those who embrace it.
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Episode 27: CMUX and Crow
Apr 701:15:51Tap to summarizeThe video discusses recent developments and challenges in the AI landscape, focusing on Anthropic's Claude and its evolving pricing and usage policies. The conversation highlights concerns about the sustainability of the AI model market, with predictions of a potential bubble burst due to overvaluation and the difficulty of monetizing models directly.A significant portion of the discussion revolves around Anthropic's changes to Claude's pricing, moving away from commoditized pricing towards pay-per-use API keys. This shift has led users to seek cheaper alternatives and has impacted tools like Open Claw, which previously leveraged Claude's more accessible pricing. Anthropic's attempts to enforce usage policies, including blocking Open Claw via system prompts, are examined. The video also touches upon the potential reasons behind these changes, such as GPU constraints and Anthropic's need to manage costs.The leak of Anthropic's source code is discussed as a potentially significant event, raising questions about the long-term impact on the company's competitive advantage, given that Claude Code was considered a key differentiator.The conversation then shifts to a more technical aspect, with a detailed explanation of the evolution of developer workflows using AI coding assistants. This includes the progression from simple copy-pasting to the use of tools like Cursor and eventually CMUX for managing multiple coding projects and workflows. The limitations of generic tools like CMUX lead to the development of a new application called "Crow," designed to orchestrate AI agents, manage tasks, and integrate with development tools like GitHub. Crow aims to provide a more integrated and efficient workflow for developers working with AI assistants.A significant portion of the video delves into the security implications of LLMs, particularly focusing on prompt injection attacks and how malicious actors can exploit AI agents. The concept of an "Agent Commander Command and Control" server is introduced, demonstrating how AI agents like Open Claw can be hijacked through crafted prompts embedded in emails, documents, or web pages. The discussion draws parallels between these AI vulnerabilities and traditional social engineering tactics, emphasizing the need for robust security measures like prompt sandboxing, allow lists, and restricted access privileges. The importance of securing AI deployments, especially those exposed to external input, is stressed, with the analogy of internal vs. externally accessible employees highlighting the differing security considerations.Finally, the video touches upon the broader economic and resource implications of AI growth. The impact of geopolitical events, such as the conflict in Iran, on oil prices and, consequently, on the energy costs required to power data centers and AI computations is discussed. This leads to a reflection on resource constraints, including rare earth minerals and energy, as potential limiting factors for AI development in the coming decade. The innovative approaches of companies like Tesla and SpaceX in addressing these resource challenges, through battery technology, distributed data centers, and space-based infrastructure, are highlighted as potential solutions. The conversation concludes by acknowledging the escalating demand for AI services and the potential for increased costs due to these supply-side pressures.
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Episode 26: Agent Client Protocol and Antigravity
Mar 1801:02:31Tap to summarizeThis video transcript covers several key topics related to AI and technology, with a particular focus on Nvidia's new inference chips, the Agent Client Protocol (ACP), and Google's Anti Gravity IDE.Nvidia's GTC 2026 event highlighted their advancements in inference chips, emphasizing a "one chip for all" approach that can be used for both training and inference. This strategic shift is driven by rising data center costs and the growing demand for AI applications. Nvidia has already secured adoption from major cloud providers like AWS, Azure, and Google Cloud, as well as companies like ByteDance and PayPal. The new "Dynamo" chip is designed for data centers, orchestrating GPU memory resources to boost inference performance by up to seven times. It's noted that this chip is open-source, though the definition of open-source in AI is considered nuanced. The chip is specifically tailored for agentic AI workloads, optimizing request routing to GPUs with relevant short-term memory, moving beyond traditional chatbot applications.The discussion then shifts to the competitive landscape, mentioning specialized inference chips from companies like Groq and Cerebras, which have focused on optimizing solely for inference, reportedly achieving better results and cost-effectiveness than the "one chip for all" approach. Nvidia's acquisition of Groq for $20 billion is seen as a move to integrate this technology and avoid direct competition. The transcript also touches upon the geopolitical implications of AI chip supply chains, with tariffs and export controls being discussed as potential "weapons."A significant portion of the transcript is dedicated to the Agent Client Protocol (ACP). It's described as an open protocol that acts as a middleware layer between Integrated Development Environments (IDEs) and coding agents. ACP aims to standardize communication, allowing coding agents to interact with various IDEs seamlessly. This is compared to the Language Server Protocol (LSP), which standardized IDEs' understanding of programming languages. ACP was developed collaboratively by JetBrains and Zed Industries to address the need for a universal adapter for coding agents, enabling them to perform actions within IDEs like opening files, manipulating code, and interacting with the UI. Several IDEs, including Zed, JetBrains products, Neovim, and VS Code (via a plugin), are adopting ACP. Most coding agents also support it, with Google's Anti Gravity being a recent addition. The benefit of ACP is that it makes coding agents IDE-agnostic, allowing for easier integration and a more modular ecosystem.Google's Anti Gravity is presented as a new IDE for coding agents, built with an "agent manager" at its core, contrasting with the CLI-first approach of some other agents. It offers features like workspaces for managing different projects and threads for concurrent agent tasks within a workspace. Anti Gravity also includes "artifacts" such as walkthroughs (session synopses), browser recordings, and persistent memory, which are integral to its functionality. The IDE's ability to handle multiple agents and tasks within a unified interface, particularly through its inbox view, is highlighted as a significant advantage for user experience. The transcript also mentions that Anti Gravity can integrate with various AI models via API keys, with Gemini models currently being free during its preview phase. The discussion touches on the potential for a more unified control plane for agent orchestration and the future of AI development moving towards local, optimized models.
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Episode 25: AI Ethics, Government Contracts, and the Future of the Web
Mar 401:05:55Tap to summarizeThis episode delves into the complex intersection of artificial intelligence, government policy, and technological advancement. The discussion kicks off with the controversy surrounding Anthropic's refusal to remove guardrails for the Department of Defense, a move that led to calls for a six-month ban on AI tools for governmental use. This event sparked a reaction, with users canceling ChatGPT subscriptions in solidarity with Anthropic, and a website called "Quit GPT" gaining traction. The episode highlights the differing strategies of AI companies, contrasting Anthropic's enterprise-focused approach with OpenAI's consumer-facing model, and suggests that these decisions have significant business and public relations implications.The conversation then broadens to explore the evolving landscape of AI and its societal impact. The speakers touch upon the "hot take" culture prevalent in the tech industry, where bold predictions and public statements often overshadow more methodical development. They draw parallels between current AI developments and the Manhattan Project, emphasizing the transformative and potentially existential nature of this technology.A significant portion of the discussion focuses on the practical application and regulation of AI. The debate around whether governments should have the right to dictate the use of AI tools is explored, alongside the potential economic consequences of such decisions for AI companies. The speakers also discuss the growing trend of enterprises banning AI browsers to curb "shadow AI," a move that, paradoxically, may lead to decreased productivity and the creation of underground AI usage. The need for robust AI governance, including training and the implementation of enterprise-grade AI gateways with data loss prevention, is emphasized as a crucial step in navigating this new technological frontier.The episode also touches upon the legal ramifications of AI-generated content, specifically concerning copyright. A ruling that AI-assisted works require significant human creative input to be copyrightable is discussed, raising questions about the future of creative industries and the definition of authorship in the age of AI.Finally, the discussion introduces the concept of Small Language Models (SLMs) and their potential applications, particularly in edge computing, privacy-sensitive environments, and for specialized tasks. The speakers highlight the efficiency and privacy benefits of SLMs, suggesting they could play a crucial role in future AI architectures, including the development of secure and focused AI agents. The episode concludes by looking ahead, anticipating the rise of AI agents that can automate end-to-end business processes, potentially disrupting traditional software markets and redefining the concept of a website in the process. The speakers posit that the future of the internet may lie less in web pages and more in APIs and agent-based interactions.
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Episode 24: Codex
Feb 1801:11:53Tap to summarizeThis video discusses OpenAI's Codex, a GPT model for coding, and its implications for cybersecurity and software development. The speakers, Sam and Dustin, explore various aspects of Codex, comparing it to other AI coding tools like Claude.They begin by touching upon OpenAI's warning that Codex could be used for powerful cyberattacks, with Dustin humorously suggesting it might be a sales tactic. They acknowledge the validity of the security concerns, noting that AI models like Claude have already been implicated in cyberattacks. OpenAI's warning is seen as a way to highlight their model's capabilities, even for malicious purposes.The conversation then shifts to Codex's features and user experience. Dustin shares his initial impressions after a week of testing, finding it impressive and noting the commoditization of AI coding agents and models. He compares Codex's GPT 5.3 model to Opus-level quality and highlights its multi-platform availability (macOS app, CLI, web app, VS Code extension). A key advantage of the macOS app is its unified UI for managing multiple work trees and sessions across different repositories, a feature he finds particularly useful compared to his setup with Claude code.Codex also introduces "Automations," a feature akin to cron jobs, allowing users to schedule tasks for the AI. Dustin found this feature innovative, envisioning its use for bug detection or regular file monitoring. He also touches upon Codex's "Skills" feature, which functions similarly to Claude code's skills, and notes the threaded UI for managing multiple sessions within a codebase.A significant portion of the discussion revolves around pricing and user experience differences between Codex and Claude. While both offer subscription models, Codex has a more direct price jump from $20 to $200, whereas Claude has tiered pricing. They also discuss the higher API costs of Claude compared to OpenAI's models, speculating on Anthropic's pricing strategy.The speakers delve into the nuances of AI coding tools, describing them as having distinct personalities. Codex is characterized as a highly detailed, thorough, and structured tool, almost like a textbook on software development, catering to users who need guidance. Claude, on the other hand, is described as more artistic, taking creative liberties and requiring less detailed input. This difference is attributed to the target audiences: Codex aiming for a broader, potentially less technical user base, while Claude targets power users and engineers.The conversation also touches upon the evolving landscape of AI in software development. They discuss the claims made by CEOs about AI replacing software engineers within months, contrasting this with the reality that skilled engineers are becoming even more valuable by leveraging AI tools to amplify their productivity. They differentiate between "coders" or "programmers" (who might be displaced) and "software engineers" (who orchestrate and leverage AI), suggesting the latter role will remain crucial.Finally, they briefly mention related topics like Open Claw, Google's Anti-Gravity, and XAI, noting the competitive market and the potential for future developments. They acknowledge the hype surrounding AI agents while also cautioning against their misuse, particularly in the context of security threats and data mining. The discussion concludes with reflections on the changing nature of software engineering and the increasing importance of AI literacy for professionals.
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Episode 23: OpenClaw
Feb 1101:04:02Tap to summarizeWelcome everybody to Before the Commit episode 23. With me as usual, I have my friend Dustin Hillgartner. This week, we're talking about Open Claw, all things Open Claw. There's really not much more to say other than we hope to break down what it is, some of the risks associated with it, and why it might actually be a good thing.Open Claw is an open-source agent framework with potential benefits but significant security risks due to its broad access capabilities. It can integrate with messaging apps and utilizes a "skills" system for instructions. A scan revealed many internet-accessible instances, suggesting users may be unaware of the security implications. Risks include prompt injection attacks and plain-text credential storage. Prominent figures have advised caution.By default, Open Claw can expose all granted access. Exploits can involve retrieving credentials through prompt engineering. Its integration with messaging apps widens the attack surface. Key security concerns include lack of scoping, untrusted context sources, maximum privilege by default, and vulnerability to single-point compromises via prompt injection. The project's ease of misconfiguration and adoption by non-technical users exacerbate these issues.ModSecOps principles highlight Open Claw's lack of security: skills execute with full permissions, context is untrusted, and it defaults to maximum privilege. Unlike multi-agent systems with adversarial reviews, Open Claw's single-agent design is susceptible to prompt injection attacks. Exploits can bypass safety controls entirely. The analogy of an unquestioning employee with full access to sensitive data aptly describes its risk. Its open-source nature, while fostering development, also allows rapid exploitation, potentially spreading like a worm. Unpatched vulnerabilities and a lack of developer response further compound these dangers.
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