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Selçuk Karayel’s Yudum.NET rebuild puts its IRC server, account and channel services, web interfaces, and Lua runtime in one Go process. The architectural payoff is one account and shared state across raw IRC, a browser client, and the website; the cost is a larger, single failure domain. The source’s title calls the network 28 years old, but its account does not establish that history.
Why make every client the same person?
An IRC network can offer a web interface and account services without making them truly part of the same system. In a split design, an IRC daemon, a separate services daemon, and a website may each maintain or consult different representations of users, channels, permissions, and messages. Keeping those views aligned requires protocols and synchronization.
Karayel describes a different organizing constraint: one person should remain the same person regardless of how they connect. In Yudum.NET, the IRC server, services, HTTP side, and Lua runtime live in one Go binary. Services are ordinary Go packages that use the server’s in-memory user and channel state and the same database. That removes a server-to-server services protocol and a class of synchronization problems between separate state owners. It also means these components share a failure domain: a process failure can affect the IRC service and its web-facing functions together.
This is the author’s account of the implementation in “One process, one account — the architecture behind rebuilding a 28-year-old IRC network in Go,” published September 30, 2026, and a republication with additional details. The repository and reported tests are not independently verified here; the architecture is a case study, not a universal recommendation.
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How do different login methods resolve to one account?
The credentials and transport vary by client, but the reported destination is a common account resolver:
| Client surface | Reported authentication route | Shared result |
|---|---|---|
| Raw IRC client | SASL PLAIN or EXTERNAL, or NickServ | Resolves to the account used by the other surfaces |
| Website | Session cookie | Resolves to the same account |
| Browser app | Short-lived token | Resolves to the same account |
Unifying identity is not the same as forcing every client to use one login mechanism. The server accepts surface-appropriate authentication and then maps it to a shared account. Karayel also reports normalizing account names before they become map or database keys. That matters because case handling can differ across browsers, Go, and SQL; inconsistent normalization can make equivalent-looking names behave like different identities.
Message delivery follows the recipient
In the account model described in the republication, delivery depends on whether the recipient is connected, not on which client the sender used. A connected recipient gets a live protocol message and the stored copy is marked read. If the recipient is offline, the message is stored unread for a later visit. Offline history is kept only between mutual friends, according to that same account.
This is the useful consequence of shared identity: message history and permissions can belong to the account rather than to a particular browser session or IRC connection. It does not mean every protocol is identical; each client still needs a way to authenticate and receive its own representation of events.
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What changes when services share a process with the IRC server?
The key design choice is direct access to shared state instead of coordinating copies over a network protocol. That can remove an entire synchronization path, but it trades away some independence between components.
| Concern | One-process approach described here | Split IRC, services, and web approach |
|---|---|---|
| State consistency | Services use the server’s in-memory user and channel structures and the same database. | Separate components need a protocol or another mechanism to keep their views aligned. |
| Failure isolation | Components share a process and therefore a failure domain. | Separate processes can isolate some failures, depending on their dependencies. |
| Independent scaling and deployment | Components are packaged together, reducing independent deployment and scaling choices. | Components can be scaled or deployed separately, at the cost of inter-service coordination. |
| Operational coordination | There is no separate server-to-services protocol to maintain. | Service protocols, reconnects, and synchronization behavior become part of the system design. |
The split-system trade-offs in this table are architectural reasoning, not measured comparisons. The author says the unified approach avoids synchronization bugs and reconnect bursts associated with coordinating separate views, but the published account does not provide a controlled comparison. A short-lived bridge process reportedly opens the same port using SO_REUSEPORT while a new binary starts, keeping the HTTP side available during deployment. That deployment measure does not change the broader fact that the main components belong to one failure domain.
Keep dependencies pointed inward
The reported package structure has handlers call content logic, content logic call the data layer named veri, and lower layers avoid calling upward. The veri layer is described as the sole owner of shared records such as accounts, settings, and messages. This gives shared state a defined access point even though the server and services run in one process; one process alone does not guarantee clean ownership or prevent every race and inconsistency.
How did SQLite contention shape the data layer?
SQLite permits concurrent readers but serializes writes. Karayel reports that the first design used one connection for everything, with SetMaxOpenConns(1). In a stress test with 32 concurrent requests, the author attributed about half the waiting time to requests waiting for that single connection; reads were queued behind other reads as well as writes.
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The reported response was to separate the connection roles:
- Writer connection: handles writes and transactions.
- Read pool: uses WAL mode and
query_only(ON)so its connections serve reads rather than becoming additional writers.
The distinction is not a claim that SQLite now has multiple simultaneous writers. It lets read work avoid waiting behind reads on the one-connection pool while retaining a controlled write path. The article also describes reducing page caches per connection and using shared memory-mapped I/O after a setup with 16 connections and 32 MB of cache per connection reportedly pushed memory toward 915 MB. Those are author-reported observations, not an independent benchmark or a guarantee that the same settings suit another workload.
How does the server keep slow clients from stalling a channel?
Karayel reports giving each IRC client a bounded outbound queue, with queue size configurable by connection class. When the server broadcasts, it makes non-blocking sends to member queues rather than waiting for each recipient to read. If a client’s queue is full, that client is disconnected with “SendQ exceeded.” The intended boundary is explicit: a slow reader should not hold up delivery to everyone else in the channel.
A bounded queue makes overload visible and finite rather than allowing an unresponsive client to accumulate unlimited pending output. The trade-off is that a client exceeding its queue capacity loses its connection and must reconnect; the implementation’s specific queue limits are not stated in the account.
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Why move Lua hooks off the message-sending path?
The Lua state is described as not safe for concurrent goroutine access, so the implementation protects it with a single lock. Initially, hooks ran synchronously on the sender’s connection loop. An outbound HTTP call could hold the lock for seconds, delaying messages from users across channels while script work completed.
The reported fix is a bounded, ordered worker queue with one worker draining it. The sender enqueues work instead of running the hook inline. If the queue is full, the message is skipped for script processing and a count is recorded; the message itself is not held up waiting for the bot. Per-account bot-command rate limits are also used to constrain abuse. This isolates the user-facing hot path, but it deliberately means scripts can miss events under overload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does the Go-to-WebAssembly browser client cost?
The browser programs are compiled with TinyGo. The author reports avoiding reflection-heavy packages and calling the browser’s JSON.parse through syscall/js. The port also required attention to WebAssembly’s 32-bit int, so explicitly sized unsigned integer types are used where appropriate. Go goroutines in this environment are cooperative rather than independently scheduled native threads; network work is connected through event-loop callbacks and code must yield appropriately.
These constraints affect implementation choices, not just download size. The reported webchat build is 5.8 MB, or 1.9 MB gzipped, while the site build is 1.5 MB, or 0.5 MB gzipped. The account also mentions a strict Content Security Policy and a UI event-delegation layer. No comparative browser-performance results are published, so the reported file sizes should not be treated as evidence of a particular startup time or user experience.
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How do gateway, voice, and games fit the shared-account model?
The republication describes HTTP long-polling as the browser-to-gateway transport in production, with WebSocket and WebRTC data channels mentioned as alternatives. The gateway reportedly uses WEBIRC so the IRC server sees the client’s real IP. The account also describes peer-to-peer WebRTC calls, moderated voice rooms with server-held room state, and one-to-many streaming over WHIP/WHEP. These details show multiple communication paths around the IRC core; no comparative performance results are given for them.
Keep hidden game information on the server
For games, the author describes a shared engine divided into rules, drawing, and bridge packages. The server validates legal moves and computes the view each seat is allowed to see. In a card game, an opponent’s hand is exposed as a count rather than as card identities, and spectators receive no hidden game state. The browser renders the supplied view and sends a move chosen from legal moves the server supplied. This is server-authoritative validation: the client can present an action, but the server decides whether it is legal and what state the player may observe.
What scale has been reported, and what remains unproven?
Karayel’s September 30, 2026 article reports the following project figures. They are the author’s measurements and descriptions, not independently verified results:
| Reported item | Figure and qualification |
|---|---|
| Go code | About 300,000 lines, excluding 265 test files, as reported by Karayel in 2026. |
| Sandboxed Lua | About 13,000 lines, as reported by Karayel in 2026. |
| Deployment host | One VPS with 6 vCPUs and 12 GB RAM, as reported by Karayel in 2026. |
| Server binary | 35 MB, statically linked and without cgo, as reported by Karayel in 2026. |
| Webchat WebAssembly | 5.8 MB uncompressed and 1.9 MB gzipped, as reported by Karayel in 2026. |
| Site WebAssembly | 1.5 MB uncompressed and 0.5 MB gzipped, as reported by Karayel in 2026. |
| Resident memory | About 550 MB RSS, as reported by Karayel in 2026. |
| Earlier cache configuration | Memory reportedly approached 915 MB with 16 connections and a 32 MB page cache per connection, as reported by Karayel in 2026. |
| Database stress test | With 32 concurrent requests, the author attributed about half of waiting time to the single-connection design, as reported by Karayel in 2026. |
The article does not publish a user count, an independent benchmark, or a completed load-test result for several thousand concurrent browser clients; the larger gateway test is described as still underway. The reported host, memory, and binary size are therefore a deployment snapshot, not proof of a particular concurrency ceiling or comparative efficiency.
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