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Here is the same task. Claude spent almost a minute on it alone, while the Jeff and Claude combo handled it in 20 seconds, which is three times faster.
Learn how combining Jeff and Claude AI agents can speed up coding tasks by 3x, optimize costs, and when their use is most effective in real workflows.
Key Takeaways
- Combining Jeff and Claude can significantly speed up and reduce the cost of AI-assisted coding tasks.
- Jeff excels at fast, rule-based decisions, while Claude is better for nuanced understanding and complex outputs.
- Not all tasks benefit from Jeff; simple tasks may become slower and more expensive with its addition.
- Confidence metrics enable efficient task routing between Jeff, Claude, and humans to optimize workflow.
- Testing your own tasks with the provided skill is crucial to determine if Jeff integration is worthwhile.
What the video covers
- Claude alone took nearly a minute to complete a task, while the Jeff and Claude combo completed it in 20 seconds, making it three times faster and cheaper.
- Jeff is designed for quick decision-making tasks like categorizing or scoring, while Claude handles complex reasoning and full-text generation.
- Jeff returns three types of answers: choice, score, and boolean, each with confidence metrics to decide if escalation to Claude or a human is needed.
- Using Jeff makes sense when there are many similar inputs with clear rules, such as sorting user messages or distributing support tickets.
- For simple tasks, adding Jeff can slow down the process and increase costs, as shown in a React prototype example.
- A testing skill is provided to help users evaluate if Jeff is beneficial for their specific tasks, accessible via a Telegram channel.
- Users can obtain an API key for Jeff from the Typeface AI website to integrate and test the system locally.
- In a complex task involving 24 user messages, Jeff quickly categorized inputs, while Claude prioritized and refined the task further.
- The combination reduces unnecessary use of complex language models for simple decisions, saving time and cost.
- The video encourages viewers to experiment with the tools and share their experiences in the comments.
Chapters
- 00:00Introduction and Speed Comparison of Claude vs Jeff+Claude
- 00:49Understanding AI Agents and Their Roles
- 01:42Jeff’s Decision Types and Confidence Metrics
- 03:08Use Cases Where Jeff is Most Useful
- 04:00Simple Task Example: React Prototype Demo
- 04:55Performance and Cost Analysis of Simple Task
- 06:37How to Get and Use Jeff API Key
- 07:22Complex Task Example: Sorting User Messages
- 08:18Results and Benefits of Jeff+Claude Combo on Complex Task
- 10:28Summary, Testing Skill, and Viewer Engagement
Full Transcript — Download SRT & Markdown
Speaker A
In terms of estimated cost, the first path was 9.5 cents, the second was 3.7—so, also three times cheaper. In this run, it turned out three times faster. But here is another task; the same combo didn't speed up anything. In
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one run, it took about the same amount of time, and in another, it was slower and even more expensive. The point isn't whether Jeff is smarter or dumber than Claude; comparing them here is pointless. These are completely different tools. One makes limited
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decisions, the other understands the task and performs the work. Today, I'll basically explain what Jeff is, why it's needed, and show where their combination actually makes sense, and where you'll just be adding another cost to your process. When we talk
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about an AI agent, we usually imagine Code, Devin, or a similar system. You set a task, the agent reads files, chooses a tool, writes code, and checks the result. But within that work, small decisions are constantly repeated.
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Which skill to open, which category to assign a task to, whether a human is needed, if it's a critical error or a regular feature request, what the next step is according to project rules, and so on. Claude knows how to answer these
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questions. Except every time, we invoke a full-fledged language model, give it context, and ask it to generate an answer. Even though sometimes we just need one category out of six. We just need to choose. And that is exactly
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what Jeff is built for. It doesn't build interfaces, write research, or replace Claude. It receives data, a list of allowed answers, and returns a decision in a pre-defined format. In a working pipeline, Jeff can quickly process a stream of incoming data.
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Claude receives a pre-prepared structure, checks the questionable parts, and does the part where full text, reasoning, or action is required.
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But what does Jeff return? Jeff has three basic types of questions, or you could call them input tasks. The first is "choice." It selects one option from a list, for example: bug, interface issue, billing question, new feature, or positive feedback. For example,
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determining which category a bug belongs to. We already have specific categories. We just need to assign it to one of them. The second type is "score." It assigns a rating on a given scale, for example, how critical an
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issue is or how well a message matches a certain topic. And "bool." It returns the probability of a "yes" answer. Well, it basically just says yes or no—for example, is human intervention required? Both choice and score have a
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confidence metric. This can be used as a toggle. Confident decisions proceed automatically. Questionable ones are sent to Claude or a human. The principle itself is what matters here.
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Jeff doesn't need to come up with a fancy answer. We limit the solution space in advance, and the program determines what to do with the result.
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That is exactly why Jeff cannot be used instead of Claude. If you ask him to build a document or a whole website, at most he will choose which skill is needed for it. But the screen or document itself must be built by the
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executor. Well, in this case, it could be GPT, Claude, and so on. If everything is clear to you, and you like this kind of content, be sure to subscribe and like it. This will really help me with promotion on YouTube. So,
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where is this useful? Role separation is interesting where we have many similar inputs and clear rules. For example, you need to sort hundreds of user messages, distribute support tickets, choose a skill from a large library—I have 112 accumulated there,
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for instance—or check an agent's actions before execution. I think these examples show a pattern where we have a huge number of parameters that we just need to choose from. Jeff works great here. When there is one question and
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the answer is obvious, an additional call just lengthens the chain. That's why I'll first show an example where Jeff is almost unnecessary. I've built this layout, this canvas, for testing.
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And here we will have Jeff working separately, Claude on its own, and the Jeff plus Claude combo. And the task here is this very basic, simple one.
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Build a working React prototype of a demo registration form using ready-made components. One step, only two mandatory fields: name, work email, and so on. You can read the rest yourself.
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In short, build such a modal with a form. The task is quite trivial. Let's see how these tools handle it. I've already connected the Jeff key. Let's launch the task execution. Let's see how they cope. Jeff immediately made a
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decision on which specific skill he needs to use. This skill is called React Layouts. He spent, well, about a second on it. Claude finished on its own, as did Jeff plus Claude. And what do we see? Here, the text is slightly
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different. Well, the task is, by and large, trivial, as I said earlier. We already have a ready-made design system and a component base; we need to assemble these modals using it. Here we can open these prototypes. Everything works here. There is validation. Okay.
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Here is Jeff plus Claude. Let's take a look. Well, it's pretty much the same thing here, right? He only changed the text here. You can also look at the specification of this element. What checks we had, the basics were set up,
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like the requirement for a single step, and so on. All this was built into the prompt. Both handled the task, and handled it well. Now let's see how much money we spent on this. The task, as I have actually repeated many times
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already, is simple. And here we found that the total travel time without this Jeff was shorter: 5 seconds versus almost ten, and the cost was even less.
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Here we have 1 dollar, here half a dollar. So, what was the task of Jeff here, by and large? He needed to choose which skill to use and verify that everything went well. And for such a small task, we see that it makes
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absolutely no sense to use this Jeff. The task ran 4.5 times slower with him and turned out to be, well, about 50% more expensive. With such amounts, there is no real point in comparing, but we see it's a small task. There is
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no point in Jeff at all. The next run will be more serious. We will look at a large task and see if Jeff is needed there. And in general, you will, of course, have your own specific tasks.
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And it's hard to understand exactly how well your tasks are suited for using Jeff. That is why I specifically put together a skill that will allow you to test it on your own tasks, just like on the canvas, to see if it will be more
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convenient and faster to use Jeff or not. You can get this skill in my private Telegram channel via the QR code on the screen or the link in the description. And when you download and install this skill, you need to get an
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API key for Jeff from somewhere. You will insert it here. And where can you get it? Just go to the Typeface AI website, go to the API Keys tab, and click "create key." Give it a name, copy the key, and that's it—paste it
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into this dashboard that will also launch locally for you. As far as I know, you currently get about 5 dollars of free usage. It, as you can see, melts away very slowly. Therefore, to test and understand whether you need it
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or not, it will be quite enough. So, now for the second, more complex task.
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We have 24 user messages about working with the service. Let's see, what is this thing again? It was fine yesterday, but not today. Some kind of complaint. Someone couldn't log in somewhere, or they don't like something. I drag a
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task to "Done" and get a 500 error. We really need a calendar with all the team's deadlines for the month. In general, some hypothetical application for team work, for managing all tasks, tracking them, and so on. And people
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are complaining: "It's unclear here, or something else is wrong." All these errors are different, belong to different categories; some have a clear e
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dig deep and understand what is actually happening here. We have several categories. Further analysis, confusing interface, technical failure, new feature, payment/tariff, positive feedback, and clarifying what the user doesn't understand. There are many categories. And here, let's see how Jev
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handles it on its own, Claude on its own, or Jev plus Claude. I mean, we have already discussed this. This is right up Jev's alley: assigning specific entities—in this case, these messages—to the appropriate categories. Let's run the analysis and
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see what we get. Starting with Jev, it sorted everything out in, say, a second , immediately putting everything into categories. Then Claude, on its own, also did an initial sort, but here it also starts thinking, prioritizing, distributing, and so on. We'll find out
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now how long that will take. Besides the category, we also need to assign urgency to the tasks here. Then, from the entire queue, prepare up to four tasks for the team with links to the original messages. Last time the CLI
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crashed, so I launched another run. So, here it also took 1.6 seconds, and Jev independently sorted the queue and determined where everything goes. But at the same time, it cannot be considered that it fully completed the task. Why? Because if we open the tasks
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tab here, we will see that Jev returned categories and urgency. To write tasks, a generative performer is needed. Jev does not write these tasks for us. It cannot do that. It specifically determined which categories the tasks should be assigned to. That's it, it
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finished there. Now Claude managed on its own in 59.5 seconds, or rather 59.77 for the task. It created, defined the problem, suggestions, and criteria for completion, forming a full-fledged task from the small initial problems we saw. Well, in principle, we can see
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them right now. Fix the access leak to private boards via a direct link. Task one. Everything is there; it describes the problem, what needs to be done, and how we will accept it. And it provides a link to what the original task was.
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Opened someone else's private board via a direct link. I am not a member of that space, but I see the tasks. That's it. It has already fully completed the task, yes, but if we look at just the sorting—what goes where—it spent
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6.52, almost 7 seconds on that. While here, the sorting alone took us 1.6, so the difference is significant. If we have a huge dataset, a huge number of tasks, and so on, the savings are corresponding. Basically, for such a
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routine process of simply sorting into categories, we are connecting a language model, meaning we are using a more complex tool than the task actually requires. We spend extra money , waste extra resources, and waste extra time. What do we end up with? The
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Jeff plus Claude task is three times faster and three times cheaper. Claude handled writing tasks, checking some controversial tickets, and identifying them. So, Jeff here doesn't just blindly take all these tasks and strictly define them into groups. If he
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understands that a controversial decision arises somewhere, he hands it over to Claude as a more complex language model, which can reason about things and so on. That is why the analysis here took longer than if Jeff were used separately. And it's exactly
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the same here. He created all these tasks. There are links to specific requests. All of this works. And the classification by Jeff itself came out to the same 1.59 as in the very first run using Jeff alone. Then there was a
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re-analysis of controversial items, and the task for the team was created by combining Claude Haiku and Claude Sonnet, depending on how complex a given task was. So, in the first path, Jeff answers 48 questions in one request, provides a category and
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urgency for each message, and his work ends there. He provides a sorted queue but does not write tasks. In the second path, Claude classifies all messages, and then collects tasks for the team in a separate request. In the combination,
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Jeff first processes the entire queue. We set a simple rule: if confidence is below 80%or data is insufficient, the message is re-checked by Claude. After that, Claude receives the entire queue and writes the tasks. Again, I tried to
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make the test as objective as possible. That is, we use different models for different tasks. We combine Sonnet, Haiku, we combine Jeff, and we set a confidence threshold. That same 80%, rather than just squeezing out the maximum speed figure to cast Jeff in a
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more favorable light. No, this setup is built as a miniature but mature system. Therefore, out of twenty-four messages, five went for re-checking in the last Jeff plus Claude run. Claude did not re-classify the other 19. It is not
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necessary. Jeff did all of that. And all the runs identified more or less the same tasks and problems.
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Accordingly, we did not lose quality here, but we made it faster and cheaper . And let me state the point once more.
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It makes sense to evaluate Jeff not as a cheap replacement for Claude, but as a decision layer within an already existing large process. It is not a replacement for Claude or any other language model. Tasks can have completely different specifics. You
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have your own environment and your own set of skills. Your own architecture for these skills, and so on. So, level up your Telegram skills via the QR code on screen or the link in the description, and test them on your own
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tasks. Feel free to comment and let us know how it went for you. Was it actually cheaper, faster, or did it turn out not to fit your needs at all?
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That would be very helpful. The main thing we cover on this channel is product design in practice, specifically for a product designer's tasks. Applications here could include, for example, initial feedback analysis, reviewing research fragments, or choosing a suitable process from a
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skill library. But synthesizing findings, formulating hypotheses, and creating interfaces still remains with the human agent. And if you want to learn how to properly integrate AI into your design processes, the AI Native Design course will help you. We go from
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concept to final prototype, creating layouts, generating visuals, conducting deep research, and following a full curriculum. You can check it out via the QR code on screen or the link in the description. And write in the comments which tasks from your own
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process you already see as something you can delegate to AI. And if you test it and share your results in the comments, that would be awesome. One way or another, no matter what neural networks do, the final decisions remain
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with us. Therefore, as I usually say, you won't be replaced by an AI, you'll be replaced by someone who knows how to use it better than you. That’s all for now. Thanks for watching. Subscribe and hit the like button. See you in the
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next videos. Oh.
Topics:AI agentClaude AIJeff AIcoding automationtask automationAI cost optimizationlanguage modelsAI workflowReact prototypeAI decision making
