僕（大倉恭弘）４コマ漫画などで携わっている、地域情報フリーペーパー「深川福々（ふかぷく）」、14号（2011年11月15日発行）が出来上がりました。
配布エリアは、下町・深川近辺は主。詳しくはこちらの公式ウェブサイトをご覧ください。
鬼平太生半可帳（３）
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Puzzle Agent (formerly Nelson Tethers: Puzzle Agent) is an adventure/puzzle game by Telltale Games, in collaboration with Graham Annable. It is the first game to come out of Telltale’s pilot project. It was released on June 30, 2010. The game was influenced by the works of David Lynch, Stanley Kubrick and the Coen brothers (most notably Twin Peaks and Fargo). This is one of two Telltale Games titles that have an original character, the other being Telltale Texas Hold’em.A WiiWare release of the game for the Wii was planned but eventually cancelled.[4]
The game begins as Agent Nelson Tethers, the sole member of the Puzzle Research Division of the FBI, is given his first field assignment. The factory that produces the erasers used by the White House has stopped production; any attempts to contact the factory are met with bizarre puzzles. Tethers must visit the factory in Scoggins, Minnesota and get it running again.
In Scoggins, Tethers is told that the factory was closed after an unidentified accident, and that the factory’s foreman, Isaac Davner, hasn’t been seen since. Further investigation is impossible because the factory is sealed by a complex lock requiring three keys.
Finally, Tethers gains entry to the factory. Inside, he finds Isaac, driven mad by puzzles given to him by the Hidden People. Tethers tries to rescue Isaac, but the Hidden People drag Isaac off before he can be saved. The factory starts back up soon afterward. Back in Washington, Tethers is congratulated for his work, and is reminded that the disappearance of Isaac is a matter for local law enforcement.
AI agents need to connect to external services to access tools, data, and capabilities beyond their built-in knowledge. That means AI agents need to be able to act as remote MCP clients, so they can connect to remote MCP servers that are hosting these tools and capabilities.
We’ve integrated the complete OAuth authentication flow directly into the Agents SDK, so your AI agents can securely connect and authenticate to any remote MCP server without you having to build authentication flow from scratch.
You can use the Agents SDK to connect an MCP client to multiple MCP servers simultaneously. This is particularly useful when you want your agent to access and interact with tools and resources served by different service providers.
The MCPClientManager class maintains connections to multiple MCP servers through the mcpConnections object, a dictionary that maps unique server names to their respective MCPClientConnection instances.
Each connection manages its own authentication context, allowing one AI agent to authenticate to multiple servers simultaneously. In addition, MCPClientManager automatically handles namespacing to prevent collisions between tools with identical names from different servers.
For more consumer use cases, deploy a remote MCP server for a To Do app that uses Stytch for authentication and MCP client authorization. Users can sign in with email and immediately access the To Do lists associated with their account, and grant access to any AI assistant to help them manage their tasks.
Regardless of use case, Stytch allows you to easily turn your application into an OAuth 2.0 identity provider and make your remote MCP server into a Relying Party so that it can easily inherit identity and permissions from your app. To learn more about how Stytch is enabling secure authentication to remote MCP servers, read their blog post.
Get started with a remote MCP server that uses Auth0 to authenticate users through email, social logins, or enterprise SSO to interact with their todos and personal data through AI agents. The MCP server securely connects to API endpoints on behalf of users, showing exactly which resources the agent will be able to access once it gets consent from the user. In this implementation, access tokens are automatically refreshed during long running interactions.
Get started with a remote MCP server that uses WorkOS’s AuthKit to authenticate users and manage the permissions granted to AI agents. In this example, the MCP server dynamically exposes tools based on the user’s role and access rights. All authenticated users get access to the add tool, but only users who have been assigned the image_generation permission in WorkOS can grant the AI agent access to the image generation tool. This showcases how MCP servers can conditionally expose capabilities to AI agents based on the authenticated user’s role and permission.
When a request comes in on the Server-Sent Events endpoint, /sse, the Worker initializes a WebSocket connection to the appropriate Durable Object for the session and returns an SSE stream back to the client. All responses flow over this stream.
The implementation leverages the WebSocket Hibernation API within Durable Objects. When periods of inactivity occur, the Durable Object can be evicted from memory while keeping the WebSocket connection open. If the WebSocket later receives a message, the runtime recreates the Durable Object and delivers the message to the appropriate handler.
Contemporary LLMs are impressive, but they are still, in most deployments, singular agents operating in linear mode: prompt in, response out. This monolithic approach is ill-suited for tasks requiring long-horizon reasoning, continuous context updating, and the synthesis of multiple expert viewpoints. In contrast, we take inspiration from how humans work in teams: through role specialization, feedback loops, and collaborative refinement.
Early experiments highlighted one of the key limitations of LLMs: an incomplete grasp of spatial relations. Basic agents often misidentified left from right or suggested tool placements that contradicted observed dynamics. Even with powerful models like GPT-4o, we found that visual information alone was insufficient. These models, trained primarily on text, lacked a stable internal representation of spatial geometry. This led us to augment visual inputs with textual feedback describing trajectories, positions, and directional cues in structured formats.
So again you travel about the mysterious town, occasionally clicking on an object or person that springs up a completely irrelevant puzzle. About 35 of them in total. Solve the puzzle, get judged on your performance, and then sit through some more stammered, awkward conversations.
Made longer, and made with the influence of some established puzzle designers, it could go somewhere. Actually use the plot as something other than a broken line on which to dangle damp puzzles. Create characters for a reason. Have it written/directed by someone who genuinely understands how to do creepy. Do something with it.
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Puzzle Agent is another one of those Telltale games that I’ve had in my Steam library for years. Well, it and its sequel, Puzzle Agent 2. They’re so similar that the second one is functionally an expansion of the first, so I’m doing them both here.
You play as Agent Nelson Tethers of the FBI’s Puzzle Research Division. (I half expected him to be British, but he’s not.) After getting the call that the White House’s supply of erasers has been cut off, you’re sent to the eraser factory in Scoggins, Minnesota. Almost every activity in this small town involves a puzzle. Who else is there to send? Unfortunately, in addition to an eraser shortage, this town hasn’t had chewing gum in a long time. That’s bad for Nelson, because that’s what he chews to concentrate on puzzles. However, there’s a bountiful supply of used gum stuck on nearly every surface in this town.
Gameplay appears to be a point and click adventure game at first, where you go around talking to people and picking gum off their walls. However, that’s merely a vehicle for the real meat of this game: the puzzles. These come in a variety of flavors: find the correct number, direct something around a map, put things together without overlapping them, complete a picture, rules and logic, and give directions. There’s probably more, but that’s as far as my memory goes. You can use a piece of gum to get a hint, but the usefulness is often dubious.
Borderlands 3 has been out for almost a year, but I haven’t bought it yet. Yes, I’m aware that it’s on Steam these days, but I haven’t been that enthused about it. I’ll probably get it in the next Steam sale, along with the Halo collection (because I threatened a long time ago to buy it if it came to Steam). I intended to play through all the Telltale games in order of release before I got to Tales from the Borderlands (and all that before Borderlands 3), but that hasn’t happened. Even without that, I recognized lots of tropes of the latter Telltale games (Walking Dead and afterwards) that I’ve heard told over the years. I’m pretty sure that there’s no kind of continuity to spoil by not playing through them in order of release.
I’ve watched Back to the Future once or twice, and I thought it was an OK movie. I didn’t think there was much to it, and the whole thing would be prevented with some very obvious safeguards. Of course, it’s hard to not notice that it’s been a cult classic for a long time. It’s the only reason that the Delorean Motor Company is known outside of niche groups. Too bad their cars weren’t as popular as those movies, and went out of business years earlier.
Like the Walking Dead, The Wolf Among Us is a series based on comic books. (I’m not familiar with either comics.) The setup is simple: all the fairytale characters were forced to leave whatever kingdoms they lived in, and are holed up in New York City scraping by. You play as Sheriff Bigby Wolf. (Bigby Wolf -> Big B. Wolf -> Big Bad Wolf) Because I’ve portrayed this guy in school plays, I’m immediately on his side. https://sites.google.com/view/word-to-unicode-converter-fre
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