A Genius With Amnesia - Victor Savkin, Nx
Description
Imagine a genie grants your wish and materializes the best engineer in the world, John Carmack in his prime, to work on your codebase. The catch: he can only see a tiny corner of it, and he forgets everything between interactions. No matter how good he is, the value isn’t there. This is what coding agents are today. We need to fix it. Speakers: - Victor Savkin (Nx): Victor is the creator of Nx, the agentic monorepo platform, and Polygraph, the meta-harness for maximum agent autonomy, with 20+ years building high-performance frameworks and build tools. X/Twitter: https://x.com/victorsavkin LinkedIn: https://www.linkedin.com/in/victorsavkin/ GitHub: https://github.com/vsavkin
Summary
Generated by gpt-5.6-solAt-a-Glance
- Verdict: Watch fully
- Core thesis: Coding agents behave like geniuses with amnesia because they are confined to one repository and one session; an agent-agnostic orchestration layer can overcome both limits by unifying repository relationships, execution state, CI, and agent traces.
- Why it matters: This is a concrete control-plane architecture for persistent, multi-agent software work across repositories, developers, machines, and model vendors rather than another attempt to improve a single agent's prompt or context window.
- Best use: Use the video as an architectural reference for designing durable agent sessions, cross-repository orchestration, shared organizational memory, and model-portable workflow state.
Executive Summary
Victor Savkin frames current coding agents as highly capable engineers who can inspect only a tiny portion of the system and forget everything between conversations. A change spanning a UI library, two modules, and a platform repository can require seven separate explanations, including re-explaining the original intent when an integration fails or a production bug appears later. The human consequently becomes both the system map and the agent's episodic memory.
He divides the problem into space and time. Spatially, repository-bound agents cannot discover all downstream consumers, consult standards stored elsewhere, update dependent projects together, or validate the complete change through downstream CI. Temporally, session-bound agents cannot recall previous decisions, traces, repository states, or the reasoning behind a change.
Savkin's solution, Polygraph, is an agent-agnostic meta-harness that analyzes repositories without changing their code, derives a unified graph of produced and consumed packages and APIs, and materializes the relevant repositories into one coordinated session. It can run an agent per repository, connect their work, manage multiple pull requests, and treat CI across repositories as one result vector so failures can be assigned to the correct producer or consumer.
The more strategically important element is persistent session state. Polygraph records intent, repositories, exact SHAs, pull requests, CI, and agent traces so another developer can reconstruct the work on another machine, potentially using a different model such as Codex instead of Claude. The presentation is product-led and does not address security, authorization, trace hygiene, scalability, or measured performance, but the underlying pattern—separating durable workflow state from interchangeable agents—is highly relevant.
Key Takeaways
- Claim: The dominant limitation of coding agents is not raw intelligence but confinement in both repository space and session time. | Evidence: Savkin compares an agent to John Carmack who can see only one-thousandth of a codebase and remembers nothing from prior conversations; he formalizes the constraints as repo-bounded visibility and absent episodic memory. | Implication: Agent infrastructure should optimize what state an agent can discover and recover, not merely select a stronger model or enlarge a prompt. | Caveat: The presentation treats these as the principal constraints but does not compare their impact with model reliability, tool errors, test quality, or ambiguous requirements.
- Claim: Repository-scoped agent workflows multiply human explanation and token usage when one logical change spans multiple codebases. | Evidence: In the UI-library example, the same change is explained across the UI repository, module one, module two, and the platform; incompatibility and a later production bug bring the total to seven explanations for essentially one change. Savkin says a change across 20 repositories can require 20 explanations. | Implication: The useful unit of agent work should be a cross-repository change or mission, with intent captured once, rather than a sequence of isolated repository chats.
- Claim: A metadata-derived dependency graph can make many repositories appear to an agent as one navigable body of code without modifying those repositories. | Evidence: Polygraph analyzes repositories a GitHub user can access and computes what each project produces and consumes, including packages and APIs. Savkin's personal graph reportedly covers roughly 300 owned repositories plus thousands of open-source dependencies. | Implication: A repository and artifact graph can serve as the discovery layer that selects context on demand, avoiding both manual repository selection and indiscriminate loading of an entire organization into a model context. | Caveat: No technical detail is provided on graph freshness, dependency inference accuracy, unsupported build systems, dynamic dependencies, or behavior at enterprise scale.
- Claim: Multi-repository writes require an orchestration and validation plane, not just broader read access. | Evidence: A Polygraph session sets up source and dependencies, starts an agent for each repository, coordinates multiple pull requests, and treats all participating CI runs as one vector. If module one fails after a UI change, the system is intended to decide whether module one needs adaptation or the UI producer introduced an incompatible change affecting every consumer. | Implication: Cross-repository agent systems need transaction-like coordination covering dependency order, CI aggregation, failure ownership, and recovery; federated context alone is insufficient. | Caveat: The talk demonstrates the intended workflow but supplies no benchmark, failure analysis, rollback design, merge-order policy, or evidence that automated fault attribution is consistently correct.
- Claim: Durable session state can make work portable across people, machines, repositories, and model vendors. | Evidence: Polygraph captures intent, repository identities, exact SHAs, pull requests, CI state, and agent traces. A coworker can materialize the session on another machine and resume it with Codex even if the original developer used Claude, with agents initialized against the corresponding code and history. | Implication: Workflow continuity should live in a model-independent state object, making the model an interchangeable executor rather than the owner of organizational memory. | Caveat: The claim of effectively identical or photographic memory depends on complete, accurate trace capture; the talk does not address hidden model state, nondeterminism, unavailable tools, environment drift, or provider-specific tool semantics.
- Claim: Organization-wide session memory can support debugging, review, retrieval of precedent, and enforcement of engineering consistency. | Evidence: Savkin says reviewers can resume the author's exact session and question an agent about recorded decisions instead of asking the author. A production bug can reference the original session so the system retrieves relevant repositories, chats, and logs; users can also search for previous vector-index work or reproduce an approach used by a respected engineer. | Implication: Agent traces can become a searchable organizational knowledge layer, but they need governance equivalent to source code, production telemetry, and privileged internal documentation. | Caveat: Centralizing every developer's traces and decisions creates significant access-control, secret-retention, privacy, provenance, and stale-practice risks that the presentation does not discuss.
- Claim: On-demand access to real source repositories can be more valuable than stuffing summaries into a larger context window. | Evidence: From an existing Claude session, Savkin asks Polygraph to add the open-source Vitest repository while working on a plugin so the agent can inspect the implementation directly and investigate deeper issues. | Implication: Prefer graph-guided context acquisition and executable source access over static mega-prompts, while imposing relevance, permission, and resource boundaries. | Caveat: Direct source access still requires intelligent retrieval and tool execution; loading more repositories can increase latency, cost, attack surface, and distraction if relevance ranking is weak.
Detailed Brief
The proposed graph models both artifacts and the history that produced them
- Claims: Savkin describes the organization's work as two connected layers: a lower repository graph containing internal and open-source artifacts, and an upper graph of agent sessions that created or modified those artifacts.; The combined graph is intended to answer not only what code exists and how repositories depend on one another, but also how a particular state came to exist through prior sessions and decisions.
- Evidence: The repository layer may include roughly a thousand organization-owned repositories and tens of thousands of open-source repositories.; Session-to-repository and session-to-session relationships allow historical work in one repository to be connected with related work elsewhere.
- Caveats: The talk does not define the graph schema, ranking method, indexing cadence, lineage guarantees, or how conflicting session narratives are reconciled.
- Implications: This is closer to a temporal engineering knowledge graph than ordinary code search: dependency topology, execution history, and decision provenance become queryable together.; The graph could become an organizational system of record, which raises the importance of data quality and provenance beyond what is required for disposable chat history.
Session retrieval can move from explicit repository selection to intent-driven discovery
- Claims: Users can begin by manually selecting repositories, but the graph can also infer which repositories and historical sessions are relevant from a natural-language task.; Relevance can operate bidirectionally: the current repository helps rank prior sessions, while the current session and patterns from similar sessions help rank repositories to add.
- Evidence: Example queries include finding every repository dependent on a particular library version and updating it, locating the most relevant repository for a proposed article, and finding prior sessions related to adding a vector index to a pull-request collection.; The system can load one or several retrieved sessions as precedent for new work.
- Caveats: No precision, recall, latency, or human-approval data is presented for automatic repository and session selection.
- Implications: Intent-to-scope resolution is a distinct orchestration capability that can reduce setup burden but needs explicit confidence signals and approval controls before broad write operations.
Notable Concepts & Terms
- Space constraint: The agent can usually inspect and modify only one repository at a time, preventing system-level reasoning and coordinated downstream validation.
- Time constraint: The agent starts each session without episodic memory, forcing humans to reconstruct prior intent, decisions, and incidents.
- Meta-harness: An orchestration layer around interchangeable agents that supplies repositories, tools, shared state, coordination, and recovery rather than acting as the underlying model.
- Unified dependency graph: A metadata graph connecting repositories through produced and consumed packages, APIs, and other relationships so relevant code can be discovered dynamically.
- CI vector: The combined state of CI across all repositories participating in one logical change, used to determine whether a failure belongs to a producer or a consumer.
- Session materialization: Reconstructing the repositories, exact SHAs, dependencies, agent histories, and related state of a prior session on another machine.
- Model-portable memory: Persisting workflow and reasoning state outside the model so work begun with Claude can be resumed with Codex or another installed agent.
- Organizational hive mind: Savkin's metaphor for making sessions and decisions from many developers available as shared context to future agents across the organization.
Operator Notes / Why Ken Should Care
- Represent an agent mission as a durable, model-independent object containing intent, artifact versions, environment data, tool events, approvals, outputs, evaluations, and lineage.
- Prototype cross-repository orchestration on one bounded dependency chain and measure explanation count, token consumption, cycle time, CI failures, and human corrections against the current workflow.
- Separate read expansion from write authority: allow broad graph discovery while requiring scoped credentials and explicit approval before modifying or opening pull requests across additional repositories.
- Define trace-governance requirements before centralizing memory, including secret redaction, tenant and team boundaries, retention, deletion, audit logs, and protection against prompt injection embedded in source or historical sessions.
- Add confidence and preview controls to intent-driven repository selection so operators can inspect why each repository was chosen before a multi-repository task executes.
- Test cross-model resumption empirically for semantic continuity, tool compatibility, environment reproducibility, and degradation when traces are incomplete rather than assuming vendor portability.
- Evaluate Polygraph or reproduce its architecture against existing OpenClaw capabilities, focusing on whether the missing primitive is repository topology, durable session state, distributed CI coordination, or all three.
Source/Metadata
- Title: A Genius With Amnesia - Victor Savkin, Nx
- Transcript words: 5055
- Duration seconds: 1199
- Timestamp note: No timestamps or chapters were present. The supplied transcript contains substantial duplicated passages near the middle and end.
Transcript
Imagine you find a magic lamp in an antique store. You rub it, a genie appears and asks how it can help. You bury the lead, so you say I need the best engineer to help with an impossible project at work. And the genie grants your wish. For me the best engineers probably joined Carmack from his id days, so you get Carmack. But the genie had a sense of humor and imposes restrictions, maybe for safety. Carmack can only see one small part of your codebase, maybe one thousandth of it. And he remembers nothing he did before. Every conversation starts fresh. That would be maddening, right? You would know there is a standard way to do stuff and Carmack wouldn't. You would have to explain the same thing over and over and over again. You would have a genius on one side and something deeply deficient on the other. And that's what agents are. Now let me walk you through an example of how many times we explain things in a simple interaction. We have four repos: UI, module one, module two, and platform. I want to change the UI and propagate the change through the system. First we change the UI library, say we change the button or whatever. That's the first explanation. Unavoidable. We have to express intent, okay? Then we publish it. We go to module one and we have to explain what just happened in the UI library so it can consume the package here. We know that that's often a different person, right? Every box in this diagram can be done by a different person. Then we discover that the published UI library doesn't work with module one. So we go back to UI and we have to re-explain the original change and the issue, right? Because it's a new agent. It doesn't know the original change. It obviously doesn't know about the issue. Let's say we fix it, right? And publish it again. We go and again we explain the new change in the context of module one, same ordeal. And we do the same for module two again. And then we go to the platform repo and we explain how everything fits together and we implement the change there. Let's imagine a week after release, a bug appears in the UI component and we have to fix it. So we start an agent in the UI repo and we have to explain again the original change from a week ago and this production issue we've seen. So we have seven explanations for what essentially is one change. And also it may not be one person making all these seven explanations, but they still occurred, right? So that's very, very typical with agents. How do we solve it? Well, there are many problems in here that contribute to this experience, but they roughly fall into two categories. The first one is that an agent essentially is repo bound. The agent sees and changes generally one repo at a time. It never sees the whole system, which can be hundreds or thousands of repos. So that's the space component of the problem. Second is amnesia. The agents forget the work. Every session starts with a blank slate. The human becomes a memory in this case. That's the time component of the problem. Let me look at the two closer. Take the repo boundary first. Without a model of how repos fit together, the agent leans on the human to do the research. It can't align the code with the rest of the system. It couldn't align the UI change with module one. The human didn't explain it. So a bad version shipped. It can't reliably reference best practices and standards either because those often live in other repos. Writing is even worse. The agent writes to one repo at a time. It means it can't validate changes downstream. Module one CI should have failed on the UI change, but it didn't. The agent can't update consumers at the same time, even though while making the UI change it has perfect information to do so. It knows exactly what it's doing. So the user has to explain stuff imperfectly to each consumer. Changing something across 20 repos means explaining things 20 times. A lot of developer time spent, but also a lot of talk and burn. The second category is that the agent forgets. The agent has no episodic memory. Every session is a blank slate. And the human in this case becomes the memory. Here's what the graph of your work actually looks like. At the bottom there is a repository graph. The artifacts your organization produces, plus every open source repo you depend on. Maybe a thousand repos you own and tens of thousands of open source repos. At the top there are all agentic sessions that create and modify that code. Sessions relate to each other. Repos relate to each other. So this graph is a faithful picture of the work in your organization. It describes what's there at the bottom and how it came to be at the top. That's what you want your agent to see. Here's what it actually sees: one session, one small fraction of your codebase, no memory, okay? Because it sees so little, it leans on the one who understands the system, the developer. Every developer has a part of that graph, right? In their hand, at least in the domain they know. The agent generally speaking doesn't. If this doesn't sound crazy, right? Imagine an agent that could see one file at a time maximum and can only look five messages back. Constraints again, both in space, what it can see, and time, how far in the past it could see. You would say that's impossible to work in. What we have now is similar to that crazy picture. And the more complex the organization is, the more apparent it becomes. I will show you how we solved it. Other organizations I talk to have similar solutions. So look at the problem and the solution conceptually, not a specific tool, although the tool is pretty cool. We built an agent agnostic meta harness called Polygraph. Let me show you what it does and how it fixes the issues we just discussed. The first idea that we arrived at is that if a GitHub user, any user, has access to thousands of repos, some of them they own, many of them are open source, we can analyze them and extract a lot of metadata out of them to build a unified dependency graph. No line of code changes in those repos. So that all happens on a side, right? And then we can get this metadata and feed it to the meta harness and create an illusion of one big code base the agent can read and write anywhere. This is my personal graph. I only have about 300 repos I own, right? And thousands of open source repos my projects depend on. Polygraph computes what each one produces, each repo, each project in each repo. What each project in each repo consumes, package-wise. What APIs they produce and consume and lots of other stuff, right? And it stitches this together into this one big body of code that your agent can work with. So let's see what it does, right? The first thing it does is it lets you start the session to print the relevant repositories in, right? So what it needs to do, it needs to set up the source code, install dependencies, set up an agent for each repo, wire them up so they can work together, and provide a clean, beautiful TUI to make non-trivial changes without getting lost. I will show you how it all works in a second. So that's pulling information in. Pulling information in is only one part of the story, right? Honestly, it's an easy part. Making changes is harder. If you have 10 repos in one session, it means you can have 10 pull requests, right? You need to run CI, you need to coordinate all of it, right? You need to do all this stuff, right? What if one of them fails, right? Polygraph treats all the CI as one vector. I will show you how it all works in a second. So that's pulling information in. Pulling information in is only one part of the story, right? Honestly, it's an easy part. Making changes is harder. If you have 10 repos in one session, it means you can have 10 pull requests, right? You need to run CI, you need to coordinate all of it, right? You need to do all this stuff, right? What if one of them fails, right? Polygraph treats all the CI as one vector. If you look at the earlier example, when we run CI for UI module 1 and module 2, if module 1 fails within a polygraph session, it will figure out who fixes it. Whether module 1 needs a patch or the UI component itself is wrong and incompatible with module 1, at which point everyone will need a patch, right? Polygraph lets you treat complex multi-repo change as if it was a single repo change. The same machinery, by the way, fixes episodic memory. Because we capture your work, no matter how many repos are involved, we know your intent, the repository is involved, PRs, we also capture all agent traces. Because we capture all of this stuff, we can relate it. So now we can say your work in one repo connects to another work in another repo, right? And all of that lets us restore any session, any piece of work on any machine or reference it from anywhere. And I will show you again how it works in a second. What you get is an agent with identical photographic memory of your entire organization. It understands how repos are written, how they relate, how they put together, and remembers every session from every repo by every developer, right? And that creates a completely different development experience. Let me show you. First, let's look at how we create a session. Something simple. You run a command and you pick some repositories from a list. Here's a tiny GitHub work with only three repos because it's a demo. I pick a backend and a frontend. Let's say I need to make a change that changes the API and has to update both the API and how stuff is being displayed. I need to give my session a name. I need to pick an agent from the ones I have installed. I picked Claude, but any install agent works the same way. Remember, Polygraph isn't an agent. It's a meta harness around an agent that makes them more capable. And in a second, the agent boots. And here I could interact with it as if I was in a single repo, even though multiple repos are involved, right? I could give it instructions. It's going to plan out the change. There are some cool animations in the UI as well. Eventually, it figures out how the two repos relate and what the change is. I can ask it to implement the change. My interaction with this is exactly the same as if I was working on a single repo. The fact that there are multiple repos involved is not really important, right? The only part where it becomes important is that I have multiple pull requests, right? But I also get a polygraph session where those pull requests are. If I look at the session, I will see I have a description. That description of the session, it describes the work conceptually, bypassing the repo boundary, saying we have to change stuff in this repo and change stuff in that repo. It gives me a good view of which repos are involved, pull requests involved, CI in those repos, everything I need to know. A lot of the stuff is what I would have in a single repo but many, right? And I also have all the agent logs captured as well, which is important for resuming, which I'm going to show you in a second. [SPEAKER_00] Now it gets interesting. [SPEAKER_00] I already saved one re-explanation. I didn't re-explain the backend change in the frontend repo, right? I explained the change once and I got it implemented in both repos and it's all in agreement. Now let's resume a session. Say I want a coworker to finish the backend change. Perhaps they own the backend repo. I send them the session. They resume it on their machine, right? So this time I'm sending them a session. They could run the command. Different machine, different everything. They use different terminal, right? They would reconstruct it on their machine. They don't have the session, right? They've never worked on it. They can pick an agent. The agent they pick could be a different agent, right? I use Claude in the original session. Say I want a coworker to finish the backend change. Perhaps they own the backend repo. I send them the session. They resume it on their machine, right? So this time I'm sending them a session. They could run the command. Different machine, different everything. They use different terminal, right? They would reconstruct it on their machine. They don't have the session, right? They've never worked on it. They can pick an agent. The agent they pick could be a different agent, right? I use Claude in the original session. Let's say they're using a different one, Codex. The same setup happens on their machine. Same repos, same SHAs, everything set up correctly. Agent starts in each repo like in mine, right? They all connect it again, so they work together. They all primed with a trace captured from my machine. So the backend repo agent on their machine has the same SHA and the same history. The frontend repo situation is the same. It's checked out at the correct SHA, has the agent running with the correct history. So my agent was Claude. They're Claude, but they share memory. And they could actually make changes in here as shown in a small video. But the memory sharing part is key, right? I can work, they can work, and we can share our memories, although we use two different agents and different machines. The full state of my session gets materialized on their machine. It's more about the state, right? The state of the world attached to the session is what enables them to continue my session, even though they didn't do anything with it originally. It's close to the transporter in Star Trek. A whole copy of my session is materialized on their machine so they can continue. And that's how I often work. When there is a pull request for me to review and I have questions, I usually don't ask the person. I resume their session on my machine. I can work, they can work, and we can share our memories, although we use two different agents and different machines. The full state of my session gets materialized on their machine. It's more about the state, right? The state of the world attached to the session is what enables them to continue my session, even though they didn't do anything with it originally. It's close to the transporter in Star Trek. A whole copy of my session is materialized on their machine so they can continue. And that's how I often work. When there is a pull request for me to review and I have questions, I usually don't ask the person. I resume their session on my machine. I get the exact state, fully functional, zero setup. And then I just talk to my agent about the decisions we made, right? Because all the decisions are in the traces captured. So my agent knows exactly what the other person talked to their agent. Side note, this is also useful when I want to switch from, say, Claude to Codex mid-session, when something goes down, okay? Take the earlier case I talked about where a bug lands in production. Here, I'm going to reference this session and say it's broken. Can you figure out what's wrong and fix it? The agent will look it up. We'll download what it needs. If description and high level information is enough, that's great. If not, it's going to pull relevant repos, relevant chats, agent logs, right? It's going to get all this information from the original session to reconstruct that state such that it can do the necessary fixes as shown here. Here, actually, it provided a fix, right? I only had to say this happened, there is a bug, that's it. No extra information was required for me to provide. So far, we have manually selected repos in sessions, but we don't have to, right? Instead of selecting the repos by hand, I can also tell the agent what I want. Remember, that graph has all this intelligence, right, about how repos relate. I could tell my agent, find every repo that depends on a particular version of a library and update it, right? I didn't have to select them. It knows a lot of metadata about what's going on. I can also ask loose questions. Things like, what if I want to write a blog post or an article? I could describe it and it will figure out which repo is the most relevant based on relationships between repos and what's in them. Another example. Let's say I want to add vector index into the PR collection. And I want to know if anyone at any point did something relevant in any repo that I can draw from. So in this case, if I do it, I'll see that it will find several sessions that appear to be relevant. And I can load one of them or both of them, right? It's useful for many reasons. Just one small example. It helps with best practices and consistency. Instead of doing stuff from scratch, where every single permutation is bespoke, I can make it replicate the approach used in a session by an engineer I respect. Now our code across repos is consistent. That's a big deal. There is a lot more to it, of course. If you are in a repo, I can ask for sessions, it will prioritize sessions that are relevant to the repo and vice versa. If I'm asking for repos, it will look at my session and see what similar sessions tend to bring in. There is a lot of interesting intelligence that makes it a lot more useful than it appears at first glance. Lastly, so far I've used the polygraph CLI, the meta-harness CLI to start it and then you can start Claude or Codex or whatever from within it. But you don't have to use it this way. So in this case, I'm already in a Claude session, but it works with anything. And I could just say, I actually think a separate repo would be useful. Maybe I'm working on a VTest plugin in this Annex repo and I could say, can you add the VTest repository to this session so I know what's going on? In this case, we'll engage polygraph and we'll set it up, configure everything and we'll bring the VTest library, which is the VTest repo, the open source repo to my session. So now my agent can explore it. It could figure out how it works and maybe resolve an issue I have in my repo. I much prefer this to context windows, because if I have the real code, the agent can go really deep. So deep problems I discover this way. All right. So agents are constrained in space and time. They only see a small fraction of the code base as they don't know the past. And both limits could be lifted. Polygraph gives agents access to the entire code your organization can reach. So now my agent can explore it. It could figure out how it works and maybe resolve an issue I have in my repo. I much prefer this to context windows, because if I have the real code, the agent can go really deep. So the deep problems I discover this way. All right. So agents are constrained in space and time. They only see a small fraction of the code base as they don't know the past. And both limits could be lifted. Polygraph gives agents access to the entire code your organization can reach, the one you own in open source. So it's no longer constrained in space. Any agent can bring all of it. Right. And it gives your agent a perfect memory of what happened. Every session, every decision made is within reach. Because it crosses developer boundaries, it's not per developer. The agent can have more context than any single developer. Like a thousand engineers have an organization, create all the sessions. They're all accessible to each of them. Almost like the Borg. Every agent can run, but every developer contributes to one big hive mind. So if it's interesting, my name is Victor. You can follow me on Twitter. If you want to check it out, go to trypolygraph.com and see if it works for you. Thank you. They don't have the session, right? They've never worked on it. They can pick an agent. The agent they pick could be a different agent, right? I use Claude in the original session. Let's say they're using a different one, Codex. The same setup happens on their machine. Same repos, same SHAs, everything set up correctly. Agent starts in each repo like in mine, right? They all connect it again, so they work together. They all primed with a trace captured from my machine. Thank you. They don't have the session, right? They've never worked on it. They can pick an agent. The agent they pick could be a different agent, right? I use Claude in the original session. Let's say they're using a different one, Codex. The same setup happens on their machine. Same repos, same SHAs, everything set up correctly. Agent starts in each repo like in mine, right? They all connect it again, so they work together. They all primed with a trace captured from my machine. So the backend repo agent on their machine has the same SHA and the same history. The frontend repo situation is the same. It's checked out at the same, at the correct SHA, has the agent running with the correct history. So my agent was Claude. They're Claude, but they share memory. And they could actually make changes in here as shown in a small video. But the memory sharing part is key, right? I can work, they can work, and we can share our memories, although we use two different agents and different machines. The full state of my session gets materialized on their machine. It left memory about the state, right? The state of the world attached to the session is what enables them to continue my session, even though they didn't do anything with it originally. It's close to the transporter in Star Trek. A whole copy of my session is always materialized on their machine so they can continue. And that's how I often work. When there is a pull request for me to review and I have questions, I usually don't ask the person. I resume their session on my machine. I get the exact state, fully functional, zero setup. And then I just talk to my agent about the decisions we made, right? Because all the decisions are in the traces captured. So my agent knows exactly what the other person talked to their agent. Side note, this is also useful when I want to switch from, say, Claude to Codex mid-session when something goes down, okay? Take the earlier case I talked about where a bug lands in production. Here, I'm going to reference this session and say it's broken. Can you figure out what's wrong and fix it? The agent will look it up. It will download what it needs. If the description, high level information is enough, that's great. If not, it's going to pull relevant repos, relevant chats, agent logs, right? [SPEAKER_00] It's going to get all this information from the original session to reconstruct that state such that it can do the necessary fixes as shown here. Here, actually, it provided a fix, right? I only had to say this happened, there is a bug, that's it. No extra information was required for me to provide. So far, we have manually selected repos in sessions, but we don't have to, right? Instead of selecting the repos by hand, I can also tell the agent what I want. Remember, that graph has all this intelligence, right, about how repos relate. I could tell my agent, find every repo that depends on a particular version of a library and update it, right? I didn't have to select them. It knows a lot of metadata about what's going on. I can also ask loose questions. Things like, what if I want to write a blog post, right, or an article? I could describe it and it will figure out which repo is the most relevant based on relationships between repos and what's in them. Another example. Let's say I want to add vector index into the PR collection. And I want to know if anyone at any point did something relevant in any repo that I can draw from. So in this case, if I do it, I'll see that it will find several sessions that appear to be relevant. And I can load one of them or both of them, right? It's useful for many reasons. Just one small example. It helps with best practices and consistency. Instead of doing stuff from scratch where every single permutation is bespoke, I can replicate the approach used in a session by an engineer I respect. Now our code across repos is consistent. That's a big deal. There is a lot more to it, of course. If you are in a repo, I can ask for sessions, it will prioritize sessions that are relevant to the repo and vice versa. If I'm asking for repos, it will look at my session and see what similar sessions tend to bring in. There is a lot of interesting intelligence that makes it a lot more useful than appears at first glance. Lastly, so far I've shown, I used the Polygraph CLI, the kind of meta-harness CLI to start it and then you can start Claude or Codex or whatever from within it. But you don't have to use it this way. So in this case, I'm already in a Claude session, but it works with anything. And I could just say, hey, I actually think a separate repo would be useful. Maybe I'm working on a VTest plugin in this Annex repo and I could say, can you add the VTest repository to this session so I know what's going on? In this case, we'll engage Polygraph and we'll set it up, configure everything and we'll bring the VTest library, which is the VTest repo, the open source repo to my session. So now my agent can explore it. It could figure out how it works and resolve an issue I have in my repo. I much prefer this to context windows, because if I have the real code, the agent can go really deep. I discover deep problems this way. So agents are constrained in space and time. They only see a small fraction of the code base as they don't know the past. And both limits could be lifted. Polygraph gives agents access to the entire code your organization can reach, the one you own in open source. So it's no longer constrained in space. Any agent can bring all of it. And it gives your agent a perfect memory of what happened. Every session, every decision made is within reach. Because it crosses developer boundaries, it's not per developer. The agent can have more context than any single developer. So agents are constrained in space and time. They only see a small fraction of the code base as they don't know the past. And both limits could be lifted. Polygraph gives agents access to the entire code your organization can reach, the one you own in open source. So it's no longer constrained in space. Any agent can bring all of it. Right. And it gives your agent a perfect memory of what happened. Every session, every decision made is within reach. Because it crosses developer boundaries, it's not per developer. The agent can have more context than any single developer. A thousand engineers in an organization create all the sessions. They're all accessible to each of them. Almost like the Borg. Every agent can run, but every developer contributes to one big hive mind. If you're interested, my name is Victor. You can follow me on Twitter. If you want to check it out, go to trypolygraph.com and see if it works for you. Thank you. Every session, every decision made is within reach. Because it crosses developer boundaries, it's not per developer. The agent can have more context than any single developer. Like a thousand engineers have an organization, create all the sessions. They all accessible to each of them. Almost like sort of the Borg. Every agent can run, but every developer contributes to kind of one big hive mind. So if it's interesting, my name is Victor. You can follow me on Twitter. If you want to check it out, go to trypolygraph.com and see if it works for you. Thank you. You can follow me on Twitter. You can follow me on Twitter. You can follow me on Twitter. You can follow me on Twitter.