Chris Slowe
Founding Engineer, former CTO, & Senior Technical Fellow at Reddit, Inc.
San Francisco, California, United States
19K followers
500+ connections
About
As Reddit’s founding engineer and longtime CTO, I helped build the company from the ground up, leading it for over a decade through its sale and ultimately to becoming a public company.
I’m now stepping into a new role as Reddit’s first Senior Technical Fellow, exploring new territory and focusing on what comes next.
Articles by Chris
-
An Inside Look at Reddit's Video Infrastructure
An Inside Look at Reddit's Video Infrastructure
There’s been a lot of engineering and product velocity at reddit over the last few years, which has been remarkable to…
9 Comments
Activity
19K followers
-
Chris Slowe shared thisTen years ago, I came back to Reddit. Twenty years and change ago (October 2005, if we're being exact), I got a call from Steve Huffman while I was grabbing coffee with a labmate. Paul Graham had suggested he hire me as Reddit's first engineer. I said yes before I hung up the phone. This week, I’ve decided to step down as CTO and take on a new role as Reddit's first Senior Technical Fellow. The last decade has been the honor of my career. We took a company a lot of people had written off and rebuilt it: the stack, the org, the culture, all while it was still in flight. Along the way, we built something the world uses—something millions of people rely on every day to learn, to connect, and to understand what’s happening around them. We went public. We found our footing in an AI-native internet without chasing every trend, and I’m even more optimistic now than I was then. The opportunity ahead for Reddit (and for the kind of internet we believe in) is bigger than ever. The right bet is still the one we made: be the source worth surfacing. Amit Puntambekar is stepping into Reddit’s CTO role, and I couldn't be more confident about where engineering is headed under their leadership. You'll be hearing a lot more from him, and I’d encourage you to pay attention. None of it was mine alone. Reddit has one of the best engineering teams in tech, and being in the room with them has been a gift. To everyone who built this with me over the last ten years and in the early days before all of it: thank you. More to come. https://lnkd.in/gsyMpMaP
-
Chris Slowe shared thisReddit showing up as a primary epistemic authority for GPT-4o isn’t a bug. It’s a signal. An eight-month longitudinal study out of London School of Economics tracked GPT-4o’s citation behavior and found something subtle but important: the model recycles roughly 55% of its sources across repeated queries. Not a broad crawl of the web. A narrow, stable pool. Reddit sits at the center of that pool, especially when first-party documentation is missing. Other traditional media and academic sources show up more when the model is further pushed on credibility. This doesn't surprise me at all: the content is dense, specific, and adversarial in the right way. It is not optimized for SEO. It is optimized for being wrong in public and getting corrected, much like any other peer review syste LLMs are making the same call humans make when they add “reddit” to a search query. But the interesting part is what this does to platform incentives. If citation is structurally constrained like this, then “citation surface” becomes a trust problem, not just a distribution problem. You are not competing for ranking. You are competing for inclusion in the pool. And the pool has memory. Once a surface degrades, through bots, astroturfing, or just low-signal noise, you do not just lose traffic. You risk falling out of the model’s default retrieval set. The study suggests that recovery is not fast or automatic. That is a very different failure mode than SEO decay. Authenticity at scale stops being a community metric. It becomes infrastructure. It shapes what models see, what they repeat, and ultimately what gets treated as “known.” The study argues for better observability into how AI systems select sources before agents act autonomously. But platforms have a parallel obligation: govern your content quality like the models are watching. Because they are. https://lnkd.in/gvMt3PHtBefore AI agents act for us, we need to know how AI searches for usBefore AI agents act for us, we need to know how AI searches for us
-
Chris Slowe shared thisrobots.txt was always the pirate’s code. A convention, a pact, a gentleman’s agreement. The Ziff Davis v. OpenAI ruling just made the gentlemen optional. Judge Stein’s holding is narrow but consequential. robots.txt is not a “technological measure that effectively controls access” under the DMCA. Ignoring it is not circumvention. It is closer to walking past a “keep off the grass” sign. The court says that explicitly. None of this is surprising if you have operated a platform at scale. The pact has been eroding for years. Crawlers ignore directives. Scrapers treat disallow rules as a suggestions box. The ruling does not change reality. It removes the illusion. It also exposes a gap a lot of teams are still papering over. If your data governance strategy is robots.txt plus ToS, you do not have a control system. You have a preference. Those are not the same thing. The court’s distinction between expressing intent and enforcing an access boundary is exactly the distinction engineering and legal need to align on. Policy defines what you want. Systems define what actually happens. Real access boundaries look like: • Authentication before content is served • Rate limiting that actually bites • Tokened, observable API access • robots.txt as one signal in a layered system, not the system The contributory infringement claims in Ziff Davis v. OpenAI ruling survived, so this is not over. The legal theory is still being tested. It will evolve. But the operational takeaway is already settled. If access policy matters, enforce it in code. Document intent in policy. Fine. Just do not confuse the sign for the fence. https://lnkd.in/gEgmUcYQ
-
Chris Slowe shared thisWow this one is amazing! One of the most persistent annoyances with serious LLM use is memory fragmentation. Every model has its own context window. Its own opaque “memory.” Its own idea of what it remembers about you. None of it is portable. None of it is reliable. So you end up rebuilding context. Over and over again. MemPalace might be the first thing I’ve seen that actually attacks this cleanly. Fully local. No API calls. A shared memory layer exposed via MCP so Claude, ChatGPT, Cursor, and Gemini can all pull from the same context. That alone is a big deal. But the design choice underneath it is the real unlock. They store raw interaction history, not summaries. That sounds obvious but it isn’t. Most “memory” systems compress aggressively and lose signal. This keeps the source of truth intact, then layers semantic retrieval and structure on top. The result is a reported 96.6% R@5 on LongMemEval and a ~34% lift over flat search. More importantly, it flips the ownership model. Cloud memory tools ask you to trust them with how you think. Your debugging trails. Your half-formed ideas. Your architecture decisions. That’s not a small ask. Keeping memory local, and explicitly deciding what gets shared with each model via MCP, feels like the right default. Not just for privacy, but for control and consistency across tools. 16k GitHub stars this early is a strong practitioner signal. I’m spinning this up today. The context window problem isn’t going away. If anything, it gets worse as you use more models. The tools that win are going to be the ones that give you durable, portable, user-owned memory. This looks like one of the first real attempts at that. https://lnkd.in/g-APJpSZGitHub - milla-jovovich/mempalace: The highest-scoring AI memory system ever benchmarked. And it's free.GitHub - milla-jovovich/mempalace: The highest-scoring AI memory system ever benchmarked. And it's free.
-
Chris Slowe shared thisWhere a Chief AI Officer (or equivalent) sits in the org is governance by topology. Conway's law is real. HHS just moved its CAIO back under the CIO, reversing the prior structure that placed the role alongside policy leadership. That implies two different operating models for AI governance. In one model, AI is primarily a platform and execution problem. The goal is to centralize tools, reduce duplication, enforce standards, and accelerate delivery. Put the role under the CIO. In the other, AI is primarily a strategic and risk-allocation problem. The goal is to shape how decisions get made, how power is exercised, and how accountability works when deployed systems create real-world consequences. Keep the role near policy leadership. Both are coherent. But they optimize for different failure modes. That’s why this matters. Org charts are not decorative. They are how institutions encode priorities. You move the role, and you move the center of gravity. https://lnkd.in/gXnjVqbx
-
Chris Slowe shared thisDependency management is one of those problems that feels solved until it isn't, and then it really isn't. Spencer Koch (official internal title: Security Wizard) just published a post on r/RedditEng walking through how we actually handle this, and not just the philosophy of why you should keep dependencies current. (Everyone agrees on that! No one wants to do it!!) Rather we talk about the the mechanics of how the sausage gets made at scale, including internal dependencies, version drift, knowing what to upgrade and when without lighting the build on fire. It's unglamorous work. It doesn't ship a feature. It doesn't show up in a press release. But at the scale we operate, letting this slip is how you end up with a codebase that's technically running but practically frozen. One where every upgrade is a multi-week archaeology project instead of a routine bump. The Dr. Strangelove reference in the title is earned. There's a point in every dependency lifecycle where you're riding the bomb down and hoping it goes okay. Worth a read if you're dealing with this at scale, or if you're building the internal tooling and process to keep it from becoming someone else's crisis six months from now. https://lnkd.in/g7xt_A-W
-
Chris Slowe reposted thisFAFOChris Slowe reposted thisSomething we're seeing more and more of in the Reddit-sphere is bad actors suggesting they're able to 'hack the system' or 'gamify' the space to enhance a brand's presence on Reddit by 10x 🫣 Unfortunately, that's a surefire way to get banned, blocked and ultimately have all your hard work go to waste. Reddit is cracking down on accounts that are flagged for the following tactics: - Overuse of a single post layout - Posting too often with low karma - Adding links to Reddit posts en masse - Utilising AI to create responses/posts - Using false personas So, although the idea of 'winning' Reddit sounds appealing, and being able to share those great numbers with your stakeholders is a dream come true, the reality is that Reddit is a community platform, not a marketing channel 🤔 It's a place for real conversations and opinions from real people. It's a place to nurture your own community and customers by providing value. It's not the place to post your latest blog with a million links to your product across 14 different subreddits, all within the same niche. If you want to actually win at Reddit, you should consider: - Taking it slow - Listening to your community - Understanding their pain points and feedback - Implementing those changes where you can - Providing real value and answering questions truthfully - Nurturing those long-term relationships because you really want to see your community succeed If you follow those steps, you'll be sure to see Reddit success well into the future! 🏆
-
Chris Slowe reposted thisChris Slowe reposted thisThere is rightful concern these days about a dearth of positive spaces for men, online or off. The fellas (and a few ladies, too) over at r/Bald are doing their part to build what the NY Times calls a "well of positivity" -- a place where men can be honest, vulnerable, and share in each other's wins, one shorn head at a time. https://lnkd.in/gn_hjMCuIn This Corner of the Internet, Everyone Looks Better BaldIn This Corner of the Internet, Everyone Looks Better Bald
-
Chris Slowe shared thisOne of the "standard" computational benchmarks is, of course, "can it play DOOM". Well, someone ran DOOM entirely over DNS TXT records...! No files. No disk. Just PowerShell queries reassembling a game from ~1,964 records hosted on Cloudflare's free tier, straight into memory. It's a _great_ hack, but it shows an important thing about this part of the technology stack: DNS TXT records are a globally distributed, edge-cached, effectively unrestricted delivery primitive. Most egress controls don't touch them. Most security tooling treats DNS traffic as benign. That assumption has always been a little shaky — DNS tunneling for C2 is not new — but this makes the delivery side more concrete: arbitrary payloads, chunked, resumable, zero cost, no files ever written to disk. The same technique that streams a 1990s shooter into memory could stage a payload, exfiltrate data, or bypass controls you thought were solid. The things worth revisiting in your threat model: DNS traffic monitoring (not just resolution failures), TXT record inspection at the perimeter, and tighter governance on who can write to your DNS zones via API. Cloudflare's API token model is fine, only if you've actually scoped and audited those tokens. That said, I'm delighted to find out that, once again, DOOM will run on anything. :) https://lnkd.in/gkbzZKUZGitHub - resumex/doom-over-dns: Play Doom entirely from DNS records. (PowerShell 7+)GitHub - resumex/doom-over-dns: Play Doom entirely from DNS records. (PowerShell 7+)
Experience
Education
-
-
-
Harvard University
AB Physics and Math
-
Activities and Societies: Phi Beta Kappa, Dudley House Committee
Patents
-
Travel Option Presentation
Filed US 13/869682
See patentA machine may be configured to provide a travel shopping service, not only to direct users of the machine, but also to users of other travel shopping services provided by partner websites whose operators have entered into partnership agreements with an operator of the machine. The machine may provide a common module to one or more partner websites or operators thereof. When a user views a partner website in a browser, the common module configures and causes the browser to obtain and implement a…
A machine may be configured to provide a travel shopping service, not only to direct users of the machine, but also to users of other travel shopping services provided by partner websites whose operators have entered into partnership agreements with an operator of the machine. The machine may provide a common module to one or more partner websites or operators thereof. When a user views a partner website in a browser, the common module configures and causes the browser to obtain and implement a custom module generated or updated specifically for that partner website. The custom module configures and causes the browser to communicate with the machine, for example, in response to fulfillment conditions specified by the operator of the partner website and used as a basis for generating or updating the custom module. Information obtained from the machine may then be presented by the browser to the user.
-
System and method for displaying hotel information
Filed US PCT/US2012/026285
See patentWhen searching for accommodations, although one or more accommodations may satisfy factors or criteria set by a traveler (e.g., a user), the traveler may desire to locate an accommodation that is also in a convenient area. A convenient area may be an area in which a concentration of points of interest (POIs) is high. For example, a traveler on a leisure trip may desire to stay at a hotel that is in an area with a relatively high concentration of restaurants or tourist attractions, or as a…
When searching for accommodations, although one or more accommodations may satisfy factors or criteria set by a traveler (e.g., a user), the traveler may desire to locate an accommodation that is also in a convenient area. A convenient area may be an area in which a concentration of points of interest (POIs) is high. For example, a traveler on a leisure trip may desire to stay at a hotel that is in an area with a relatively high concentration of restaurants or tourist attractions, or as a traveler on a business trip may desire to stay at a hotel near an airport or convention hall. Thus, some example embodiments provide accommodation search results to one or more users based on a concentration of points of interest. The accommodation search results may be provided graphically, textually, or any suitable combination thereof.
Languages
-
English
-
Other similar profiles
Explore more posts
-
Olivia Teich
I was CPO at Blend before their $4 billion IPO. We rejected the “forward deployed engineer” Palantir model that everyone is copying now. Here’s why: Blend could have followed the forward-deployed engineer approach. Both our CEO and CTO were early Palantir. They'd seen it work. Instead, we built a SaaS product with core components. Move fast, iterate, use data to learn. Not custom code for every client. That Palantir model is having a resurgence. Now Sierra, Decagon, and others are hiring forward-deployed engineers. But it’s not the right approach for every company. It’s fundamentally the wrong answer for Assembled’s customers. Here's what we understand about support teams and why we’re not going the forward deployed route: Customer Support teams are used to getting no resources and no support. On the surface, you'd think they'd be excited about engineers building custom solutions. "Finally, someone's offering us developers!" The reality is support teams are terrified of getting help they can't afford to keep. Support teams are wary because they know the math. They don't have budget for expensive, scarce resources. At some point the shoe drops. The engineers disappear when priorities shift. And custom solutions break. Support is left holding the bag with something they can't maintain. They're resource-starved by design. The idea of a dependency on resources they can't control is terrifying. So instead, the goal should be to give support teams the ability to manage, change, and solve their own problems. Not dependencies on engineers they can't keep. Smart, competent people who teach them to do it themselves. Palantir wasn't even a product company for years. They were a custom development shop with reusable components. Show up, build something bespoke, maybe extract learnings. That's not scalable for any but the largest support teams who need sustainability. Forward-deployed engineers are the new shiny object. I wasn’t a fan of it at Blend. And I’m not a fan today either. What I see in the customer support space is that teams need empowerment, not dependency. I want the teams we work with to be able to say: "I love that you’re partnering with me. And I love that I can do this myself too." That's the difference between building a product and building a consulting practice.
6 Comments -
Chris Tonkinson
#Clawdbot has everyone's attention right now. It's cool, no doubt. But I see it (at least as-is, and of course it's rapidly evolving like everything in this space) as more of a tech demo than a production utility. Look - the flexibility of LLM is, by definition, fuzzy and flexible and multi-reasoning capable - I get it. But Clawdbot is... everything for everyone all the time. It lacks opinionated design. A non-null set of future universes includes it evolving into a generic "hidden" implementation layer supporting more use-targeted "frameworks" that leverage the capability in a more focused and intentional way.
-
Navdeep Manaktala
Anthropic just shipped file-system agents to consumers. The architecture matters more than the feature! Cowork takes Claude Code's autonomous capabilities and removes the CLI barrier - now anyone on macOS can point Claude at a folder and let it execute multi-step workflows. Organize files, process data, create documents. The AI plans and executes independently. Why the technical approach matters Most AI tools operate in browser sandboxes. Cowork works at the OS level - executing file operations autonomously, not just suggesting edits. That's a fundamentally different execution model. Anthropic built this in ~1.5 weeks using Claude Code itself. When AI can scaffold its own tooling, development velocity changes completely. What this signals strategically The AI productivity market is bifurcating: 1. Chat-based tools scale horizontally (more users, more conversations, commoditizing) 2. Agentic tools scale vertically (fewer users, higher value, defensible if you solve for trust) Anthropic's $100-200/month pricing reflects this - they're positioning Cowork as workflow infrastructure, not consumer SaaS. The constraint is trust, not capability Claude can delete files and execute arbitrary operations. That's why this is a controlled research preview. The technical primitive works - the challenge is building trust infrastructure to scale it. Audit logs, rollback mechanisms, granular permissions - these are requirements, not nice-to-haves. Why OS-level access is the real trend Browser AI is limited by web security models. It can't access local files, email databases, or coordinate between applications without manual intervention. You feed it context, then manually move its output somewhere useful. OS-level AI sees your complete working context and executes full workflows autonomously. Pull data from email, update a spreadsheet, draft a report, sync to cloud - without human intervention between steps. Most valuable work happens between applications. Browser AI lives in one tab. System-level AI orchestrates across your entire computing environment. This is why the trend isn't just "better AI" - it's where AI runs. Browser tools compete on inference quality and UI (commoditizing fast). OS-level tools compete on integration depth and trust infrastructure (defensible, takes years to build). Where this leads If file-system agents prove out, next is inter-application orchestration. Today Cowork organizes folders. Tomorrow it coordinates across your file system, calendar, email and task manager. The highest-leverage AI applications won't live in browsers - they will live in your system with administrator-level permissions. Cowork isn't just a productivity feature. It's Anthropic testing whether users are ready for AI with OS-level access. https://lnkd.in/gAQG9UGe https://lnkd.in/gXW756p7 #AgenticAI #AI #ArtificialIntelligence
1 Comment -
Neil Tewari
The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.
91 Comments
Explore top content on LinkedIn
Find curated posts and insights for relevant topics all in one place.
View top content