Latest / AI Builds It: Easy Coding Tools / Where the Claude Fable 5 Codes Best: Claude Code vs Cursor vs Windsurf vs Copilot vs Cline/Roo for Agentic Software Engineering

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Where the Claude Fable 5 Codes Best: Claude Code vs Cursor vs Windsurf vs Copilot vs Cline/Roo for Agentic Software Engineering · AI Builds It: Easy Coding Tools

• 0:00
  SPEAKER_00: Hook.
• 0:01
  SPEAKER_00: Beyond the best code model.
• 0:03
  SPEAKER_00: Imagine telling an AI to ship a feature to production and watching it plan, code, test, commit, and even create a pull request all on its own.
• 0:14
  SPEAKER_00: Today's AI coding assistants are no longer just autocomplete machines.
• 0:18
  SPEAKER_00: They are agentic software engineers working inside sophisticated systems.
• 0:24
  SPEAKER_00: It's not enough to ask which model writes the best function.
• 0:27
  SPEAKER_00: Instead, we ask which setup turns a powerful model into a reliable coding partner.
• 0:33
  SPEAKER_00: The same claw model can perform very differently if it's used in a simple browser chat versus inside an IDE with terminal access, memory, and safety checks.
• 0:45
  SPEAKER_00: This article untangles the latest Claude model and the tools, from Anthropic's Claude Code to open source editors that harness it for real coding work.
• 0:55
  SPEAKER_00: The newest Claude model, Anthropic's latest flagship model, is Claude Fable 5, released June 2026.
• 1:03
  SPEAKER_00: Fable 5 is described as a mythos class model the company has made safe for general use, with capabilities exceeding those of any model we've ever made generally available, especially on long, complex tasks.
• 1:17
  SPEAKER_00: Anthropic's official documentation calls Fable 5 the most capable, widely released model in a family that now outperforms the older Claude Opus 4.8 on coding benchmarks.
• 1:29
  SPEAKER_00: A more powerful Claude Mythos 5, the same underlying model without some safety filters, is limited to special programs and not publicly available.
• 1:38
  SPEAKER_00: Anthropic positions Fable 5 as their go-to model for ambitious software projects.
• 1:44
  SPEAKER_00: It has a huge context window, up to 1 million tokens, and excels at maintaining context over days-long planning and coding sessions.
• 1:52
  SPEAKER_00: For example, Anthropic cites an internal test where Fable 5 migrated a 50 million line Ruby code base in one day.
• 2:00
  SPEAKER_00: Work that would normally take a whole team two months.
• 2:03
  SPEAKER_00: In short, Fable 5 is built to be thorough, proactive, and self-testing.
• 2:08
  SPEAKER_00: It even uses its new vision capabilities to check code output against designs.
• 2:13
  SPEAKER_00: Fable 5 is available on Anthropic's API as model ID Claude Fable V.
• 2:19
  SPEAKER_00: Pricing is$10 per million input tokens and$50 per million output tokens, about twice the per token cost of Opus 4.8.
• 2:28
  SPEAKER_00: For June 2026, Anthropic briefly included Fable V in its subscription tiers at no extra cost, then shifted to credit-based usage on July 23rd.
• 2:39
  SPEAKER_00: In any case, if you or a tool has an Anthropic API key with access, you can invoke Fable 5 directly via AWS Bedrock or Claude platform, just like any other Claude model.
• 2:51
  SPEAKER_00: Why coding of all tasks?
• 2:53
  SPEAKER_00: Anthropic explicitly calls Fable 5 their best coding model.
• 2:58
  SPEAKER_00: Its product page brags that Fable is our most capable model for ambitious coding projects, including large migrations, complex implementations, and multi-day autonomous sessions.
• 3:09
  SPEAKER_00: Anthropic's benchmarks show Fable 5 doubles the performance of Opus 4.8 on the hardest coding benchmarks.
• 3:17
  SPEAKER_00: With features like planning, testing, and vision, Fable 5 was designed to engineer software, not just write single functions.
• 3:25
  SPEAKER_00: Why the harness matters?
• 3:27
  SPEAKER_00: With an LLM like Claude Fable 5, the real magic or the real pain comes from the harness around it, the editor or assistant that provides memory, tools, and a workflow.
• 3:38
  SPEAKER_00: A model responding to a single prompt is fundamentally different from one working in a long-running loop with sandboxed code execution, a persistent chat history, and Git integration.
• 3:49
  SPEAKER_00: State in context, in a simple chat interface, Fable 5 can only remember what you paste in.
• 3:55
  SPEAKER_00: In an agentic harness, it can hold the entire code base and conversation in memory.
• 4:01
  SPEAKER_00: For example, Windsurf's Cascade Agent keeps awareness of everything in a developer's session and uses Claude's full context window to plan next steps.
• 4:10
  SPEAKER_00: This continuity lets the model do multi-file refactors or feature builds without losing track.
• 4:16
  SPEAKER_00: Tool access.
• 4:18
  SPEAKER_00: A plain chat model can only talk.
• 4:20
  SPEAKER_00: An agent can act.
• 4:22
  SPEAKER_00: Tools like Claude Code or Kline give Claude a virtual IDE.
• 4:26
  SPEAKER_00: It can read-write files, run shell commands, install dependencies, run tests, etc.
• 4:32
  SPEAKER_00: This eyes and hands functionality fundamentally changes what the model can do.
• 4:37
  SPEAKER_00: For instance, Klein explicitly lets Claude run terminal commands and even launch a browser to test web apps.
• 4:43
  SPEAKER_00: That means instead of asking Claude what tests to write, you can have it actually write and execute those tests.
• 4:49
  SPEAKER_00: Plans and looping.
• 4:50
  SPEAKER_00: A raw LLM is one turn at a time.
• 4:53
  SPEAKER_00: An agent framework can run that model in loops, synthesize a plan, plan mode, execute part of it, act mode, check results, and iterate.
• 5:01
  SPEAKER_00: Tools like Claude Code have built-in workflows, plan act modes that let the model plan a multi-stage change and delegate some tasks to itself.
• 5:09
  SPEAKER_00: Without this, all you get is one-shot prompts.
• 5:12
  SPEAKER_00: As Anthropic noted, Fable 5 especially shines when it can plan across stages, spawn subagents, and do self-checks.
• 5:20
  SPEAKER_00: Safety and rollback.
• 5:22
  SPEAKER_00: Agents can add breaks that chatbots don't have.
• 5:25
  SPEAKER_00: For example, Climb requires you to approve every file edit before it happens, and it automatically snapshots the workspace so you can restore any point.
• 5:34
  SPEAKER_00: Claude code can be run with a safe mode to Linux commands.
• 5:37
  SPEAKER_00: In contrast, an experimental shell agent with fewer safeguards might accidentally delete a file.
• 5:43
  SPEAKER_00: In short, the model is only half the picture.
• 5:46
  SPEAKER_00: The harness, its memory, tools, and guardrails makes or breaks a real coding workflow.
• 5:52
  SPEAKER_00: The same Claude Fable 5 will feel very different driving a VS Code plugin with instant suggestions, file navigation, and git context.
• 6:00
  SPEAKER_00: Tool-by-tool comparison.
• 6:02
  SPEAKER_00: Each AI coding product uses Claude differently.
• 6:06
  SPEAKER_00: Below we look at major agentic coding harnesses, focusing on whether and how they incorporate the newest Claude.
• 6:14
  SPEAKER_00: Anthropic Claude Code.
• 6:16
  SPEAKER_00: Claude Code is Anthropic's official VS Code terminal agent environment.
• 6:21
  SPEAKER_00: It runs a Claude model in a fully agentic mode.
• 6:24
  SPEAKER_00: As of version 2.1.170, June 2026, Claude Code now supports Claude Fable 5.
• 6:33
  SPEAKER_00: You can update Claude Code and then issue Claude-model Claude-Fable 5 to use it.
• 6:39
  SPEAKER_00: Behind the scenes, Claude Code manages long sessions, it reads your repo, plans changes, runs tools, and can even commit or open pull requests.
• 6:48
  SPEAKER_00: It maintains a running transcript and work directory for context.
• 6:53
  SPEAKER_00: You have control via commands, e.g., run tests, open files, and can push changes to Git when you're satisfied.
• 7:00
  SPEAKER_00: Model Fable 5 via Claude-Fable 5 or older Claude 4 models.
• 7:06
  SPEAKER_00: The CLI lets you pick any Claude API model or alias, e.g., Opus Plan, Sonnet.
• 7:13
  SPEAKER_00: Usage works as a command line agent or VS Code extension.
• 7:16
  SPEAKER_00: It's designed for multi-step workflows, not just one-shot completions.
• 7:21
  SPEAKER_00: E.g., it has plan mode to draft a plan before coding.
• 7:25
  SPEAKER_00: Control, you explicitly approve actions.
• 7:28
  SPEAKER_00: Every file edit is staged, but not finalized until you confirm the commit.
• 7:32
  SPEAKER_00: You can cancel or revert easily via the session transcript and post-session hooks.
• 7:37
  SPEAKER_00: Context maintains a session history and workspace.
• 7:41
  SPEAKER_00: It can remember files across turns, though it has a finite context window, up to 200k per prompt or so.
• 7:48
  SPEAKER_00: It also supports a persistent memory feature.
• 7:50
  SPEAKER_00: Anthropic calls it file-based memory, which triples Fable 5's effectiveness on long tasks.
• 7:56
  SPEAKER_00: Safety includes built-in safeguards, e.g.
• 8:00
  SPEAKER_00: safe mode, that limits risky actions.
• 8:02
  SPEAKER_00: Fable 5 itself has content filters for cybersecurity biology.
• 8:07
  SPEAKER_00: Flagged queries quietly fall back to the next safest model, Opus 4.8.
• 8:12
  SPEAKER_00: You always need to approve changes, giving you final control.
• 8:16
  SPEAKER_00: Cost.
• 8:17
  SPEAKER_00: Running Fable 5 in Claude Code consumes your Claude credits.
• 8:20
  SPEAKER_00: $10,$50 per million tokens.
• 8:23
  SPEAKER_00: In long one to two hour dev sessions, costs can add up hundreds of dollars compared to cheaper models or local alternatives.
• 8:31
  SPEAKER_00: Review ease.
• 8:32
  SPEAKER_00: Because all changes go through an interactive session, you see every suggestion and diff.
• 8:37
  SPEAKER_00: You can halt or audit at any time.
• 8:39
  SPEAKER_00: The Claude session transcripts log everything for post hoc review.
• 8:44
  SPEAKER_00: Cursor AI IDE.
• 8:45
  SPEAKER_00: Cursor is a commercial AI coding assistant, currently developer preview that integrates Claude among many models.
• 8:52
  SPEAKER_00: Cursor's interface includes a chat window, an intelligent IDE editor, and an agent mode for big tasks.
• 8:58
  SPEAKER_00: Its docs list Claude Fable 5, 300k context, as one of the selectable models.
• 9:04
  SPEAKER_00: In practice, the default cursor plan, Composer 2.5 or Google's Gemini, runs by default, but you can switch Cursor to Claude Fable 5 in the model menu.
• 9:16
  SPEAKER_00: Model.
• 9:17
  SPEAKER_00: Cursor can use multiple models.
• 9:19
  SPEAKER_00: Its tables show anthropic choosing between Claude 4X and Fable 5.
• 9:25
  SPEAKER_00: For example, Fable 5 appears with 300k context capacity alongside Opus 4.8.
• 9:32
  SPEAKER_00: Note, as of early 2026, Fable support in Cursor may require a Pro Plan or BYOK, but Cursor's docs indicate it is available.
• 9:43
  SPEAKER_00: Usage.
• 9:44
  SPEAKER_00: Cursor blends chat completion, inline editing, tab completions, and a powerful agent called plan mode.
• 9:50
  SPEAKER_00: It's mainly an IDE plugin, not a terminal agent.
• 9:54
  SPEAKER_00: It's repository aware.
• 9:56
  SPEAKER_00: It parses your code base in the background and uses that context for suggestions.
• 10:01
  SPEAKER_00: Control.
• 10:02
  SPEAKER_00: Most changes from cursor show up in your editor for you to accept or reject manually.
• 10:07
  SPEAKER_00: It also has a dedicated agent view where you give it a task, implement feature X, and it attempts the multi-file edits.
• 10:15
  SPEAKER_00: Even then, the developer reviews each change before committing.
• 10:19
  SPEAKER_00: Context.
• 10:20
  SPEAKER_00: Cursor maintains conversation context across turns.
• 10:23
  SPEAKER_00: It also has features like plan mode, which looks at the full repo and creates a checklist.
• 10:28
  SPEAKER_00: According to the cursor team, it keeps the full development session history in context for planning the next steps.
• 10:34
  SPEAKER_00: It can handle up to 1 million tokens in max mode for deep tasks.
• 10:39
  SPEAKER_00: Safety.
• 10:40
  SPEAKER_00: Cursor is cloud hosted, so the code you share goes to cursor servers with the chosen model.
• 10:45
  SPEAKER_00: The developer still inspects every change, so accidental output is catchable.
• 10:50
  SPEAKER_00: Cursor doesn't mention agenc security features, but it does integrate with your version control so you won't lose code.
• 10:56
  SPEAKER_00: Cost.
• 10:57
  SPEAKER_00: Agent mode on Cursor is paid per task or per month.
• 11:00
  SPEAKER_00: Using Claude Fable 5, if available, would burn your cursor credits quickly.
• 11:05
  SPEAKER_00: Cursor often suggests using its own optimized SWE models to cut costs 13 times faster than older Clauds.
• 11:13
  SPEAKER_00: Review, ease.
• 11:14
  SPEAKER_00: Cursor versions every plan step.
• 11:17
  SPEAKER_00: You can compare before-after for each commit.
• 11:19
  SPEAKER_00: Its UI for reviewing agent changes is polished.
• 11:22
  SPEAKER_00: You can undo whole tasks.
• 11:24
  SPEAKER_00: In chat mode, like any IDE plugin, you manually commit or discard snippets.
• 11:29
  SPEAKER_00: Windsurf Cascade IDE.
• 11:32
  SPEAKER_00: Windsurf Cascade builds itself as an AI native IDE.
• 11:36
  SPEAKER_00: It has its own internal SWE models specialized for coding, but it also supports Anthropic via Bring Your Own Key, BYOK.
• 11:44
  SPEAKER_00: Importantly, Windsurf had no direct pipeline for Fable 5 in mid-2026.
• 11:49
  SPEAKER_00: Its public docs only listed Claude 4 Sonnet Opus models, and the BYOK function was limited to only Claude 4.0, 4.1 models.
• 11:58
  SPEAKER_00: In practice, Windsurf has been in flux.
• 12:01
  SPEAKER_00: TechCrunch reported that Anthropic cut off Windsurf's first-party access to Claude 3.x and 4.x in 2025, amid rumors of a merger, forcing Windsurf to rely on third-party servers or BYOK.
• 12:14
  SPEAKER_00: Anthropic did say users could still plug in their Claude API keys, but only the older Sonnet Opus models, no mention of Fable.
• 12:23
  SPEAKER_00: Model, WinSurf's built-in agent, uses Windsurf's own models by default, the SWE series.
• 12:30
  SPEAKER_00: By enabling BYOK with your Anthropic key, you could use Claude 4 Opus Sonnet models.
• 12:36
  SPEAKER_00: Fable 5 does not appear to be officially supported in WinSurf as of mid-2026.
• 12:42
  SPEAKER_00: Even WinSurf's leader acknowledges that clients have to bring or own key for Claude, and that it's more expensive than it should be.
• 12:51
  SPEAKER_00: Usage.
• 12:51
  SPEAKER_00: Windsurf is an IDE, VS Code fork, with an AI assistant.
• 12:56
  SPEAKER_00: You give it prompts in a composer pane or select code and ask cascade.
• 13:01
  SPEAKER_00: It also automatically suggests completions.
• 13:03
  SPEAKER_00: Control.
• 13:04
  SPEAKER_00: Windsurf's agent doesn't auto-commit.
• 13:07
  SPEAKER_00: It inserts code in the editor for you to finalize.
• 13:10
  SPEAKER_00: The user remains in the loop for trusting the suggestions.
• 13:13
  SPEAKER_00: It also integrates with GitHub, Slack, etc., but any change is manual or requires your approval.
• 13:20
  SPEAKER_00: Context.
• 13:21
  SPEAKER_00: Cascade Strength is keeping a very large context of your project.
• 13:25
  SPEAKER_00: The Windsurf team highlights that it understands and reasons about long sequences of development activity and can look at everything happening in a session to guide next steps.
• 13:34
  SPEAKER_00: It also claims nearly instant responses because it heavily indexes the repo for context retrieval.
• 13:40
  SPEAKER_00: Safety.
• 13:41
  SPEAKER_00: Beyond requiring your manual approval, Windsurf's code changes happen in your IDE environment.
• 13:47
  SPEAKER_00: You still see the edits before saving.
• 13:49
  SPEAKER_00: Windsurf is cloud connected, so code is sent to its servers or your BYOK provider.
• 13:55
  SPEAKER_00: For sensitive code bases, that could be a concern.
• 13:59
  SPEAKER_00: Cost.
• 13:59
  SPEAKER_00: Windsurf is subscription-based for enterprises.
• 14:02
  SPEAKER_00: It even reaches 100 million ARR.
• 14:04
  SPEAKER_00: Using a BYOK Claud model means paying Anthropic directly on top of Windsurf fees.
• 14:10
  SPEAKER_00: The internal SWE models are optimized for speed and low cost by design.
• 14:15
  SPEAKER_00: Review ease.
• 14:16
  SPEAKER_00: Windsurf shows all AI-generated code as regular diffs in the editor.
• 14:21
  SPEAKER_00: You can undo or rerun agent tasks easily.
• 14:24
  SPEAKER_00: However, any rollbacks are your usual Git operations.
• 14:28
  SPEAKER_00: It does not have special checkpoints beyond what Git provides.
• 14:31
  SPEAKER_00: GitHub Copilot, Copilot Workspaces slash Agent.
• 14:35
  SPEAKER_00: GitHub's copilot, GitHub Chat Workspaces, now offers an anthropic mode anthropic claud agent in beta.
• 14:42
  SPEAKER_00: This is a third-party coding agent running in the copilot interface, but it is limited in the claw models it can use.
• 14:48
  SPEAKER_00: According to GitHub Docs, the supported anthropic models are only the Claude 4 series, OPIS 4.5 to 4.7 and Sonnet 4.5 to 4.6.
• 14:59
  SPEAKER_00: In other words, Copilot does not currently provide Fable 5.
• 15:03
  SPEAKER_00: Your Copilot subscription gives access to this agent, but the AI is essentially hosted by Anthropic under the Copilot Hood.
• 15:10
  SPEAKER_00: Model.
• 15:11
  SPEAKER_00: Copilot's Anthropic agent uses up to Claude 4.7, not Claude 5.
• 15:16
  SPEAKER_00: It also allows an auto mode that picks the best available.
• 15:21
  SPEAKER_00: For OpenAI fans, copilot standard completions are still powered by OpenAI's models, e.g.
• 15:28
  SPEAKER_00: GPT-4.
• 15:29
  SPEAKER_00: So using Copilot Chat without switching banks still means GPT-based suggestions.
• 15:35
  SPEAKER_00: Usage.
• 15:36
  SPEAKER_00: The anthropic agent appears as a separate co-pilot chat sidebar.
• 15:40
  SPEAKER_00: You can assign a task to it, like an issue to fix, and it will attempt to use Claude.
• 15:45
  SPEAKER_00: It's integrated with GitHub Issues PR's knowledge and can commit changes into a PR.
• 15:51
  SPEAKER_00: For normal copilot autocomplete, it stays as OpenAI behind the scenes.
• 15:56
  SPEAKER_00: Control.
• 15:57
  SPEAKER_00: Because it's tied to GitHub, when the agent finishes working, you get a normal PR diff to review on GitHub's site.
• 16:05
  SPEAKER_00: You still have to approve and merge.
• 16:07
  SPEAKER_00: Context.
• 16:08
  SPEAKER_00: The agent knows about the current repository and recent user chat, but it is not truly running days-long sessions.
• 16:15
  SPEAKER_00: It may remember previous turns in the copilot chat within that browser session.
• 16:20
  SPEAKER_00: Safety.
• 16:21
  SPEAKER_00: This is still a cloud service.
• 16:23
  SPEAKER_00: Changes go into your repo via pull requests, so you control merges.
• 16:27
  SPEAKER_00: GitHub has its own policy controls for who can enable which agents.
• 16:31
  SPEAKER_00: Anthropics Claude Safeguards, OPUS Fallback, still apply behind the scenes.
• 16:36
  SPEAKER_00: Cost.
• 16:37
  SPEAKER_00: Copilot is subscription-based.
• 16:39
  SPEAKER_00: In principle, you're paying for copilot seats, starting$10 user month, and not per token.
• 16:45
  SPEAKER_00: The anthropic usage might be included in that fee or an enterprise plan.
• 16:50
  SPEAKER_00: Review ease.
• 16:51
  SPEAKER_00: Since outputs become actual PRs or chat replies, you review them just like any code.
• 16:56
  SPEAKER_00: There's no automatic rewrite without your okay.
• 17:00
  SPEAKER_00: Klein, open source AI agent.
• 17:02
  SPEAKER_00: Klein is an open source coding agent you run in your own editor or terminal.
• 17:07
  SPEAKER_00: It's model agnostic.
• 17:09
  SPEAKER_00: You provide your own API keys for any LLM, Anthropic, Open Router, OpenAI, etc.
• 17:15
  SPEAKER_00: In practice, that means you can hook Kline up to Claude Fable 5 if you have a valid Claude API key provider.
• 17:22
  SPEAKER_00: Klein's pitch is transparency and control.
• 17:25
  SPEAKER_00: No model lock-in, and every decision is visible.
• 17:28
  SPEAKER_00: Model, totally up to you.
• 17:30
  SPEAKER_00: By default, it supports Claude, GPT-4.5, Gemini, or even running local open models.
• 17:36
  SPEAKER_00: To use Claude, you set your Claude API key in client's config.
• 17:41
  SPEAKER_00: Then it will send prompts to whichever Claude model you choose, e.g., Claude Sonnet 4.6 or Claude Fable 5, just like any API.
• 17:50
  SPEAKER_00: Usage.
• 17:51
  SPEAKER_00: Klein works inside VS Code, JetBrains, or as a CLI.
• 17:55
  SPEAKER_00: You open Kline and type what you want.
• 17:57
  SPEAKER_00: Plan and Act Mode.
• 17:58
  SPEAKER_00: It can then traverse the code base, make changes, run commands, etc.
• 18:03
  SPEAKER_00: You basically interact with it like a command line agent assistant.
• 18:07
  SPEAKER_00: Control.
• 18:07
  SPEAKER_00: Klein advertises explicit human in the loop.
• 18:10
  SPEAKER_00: It lists every change and asks confirmation.
• 18:13
  SPEAKER_00: Under the hood, it actually runs git commands, shell commands, and you see all diff hunks before they apply.
• 18:20
  SPEAKER_00: If anything looks wrong, you can reject it.
• 18:22
  SPEAKER_00: And Kline autosaves checkpoints of your files so you can roll back easily.
• 18:28
  SPEAKER_00: Context.
• 18:29
  SPEAKER_00: Klein maintains the session workspace and can remember things across commands.
• 18:35
  SPEAKER_00: It also integrates a notion of tasks you can start and resume so it can keep global state for 30 to 90 minutes or more.
• 18:43
  SPEAKER_00: However, it doesn't have a built-in long-term memory store beyond the open session.
• 18:48
  SPEAKER_00: No agents.md file.
• 18:51
  SPEAKER_00: Safety.
• 18:52
  SPEAKER_00: Very safe for your repo because it's local.
• 18:55
  SPEAKER_00: Your code never goes to Klein servers, it only goes to whichever LLM API you configure.
• 19:01
  SPEAKER_00: All actions require your approval, and Klein's built-in logging means you see the exact prompt sent and the diff returned.
• 19:09
  SPEAKER_00: It's essentially no black box by design.
• 19:12
  SPEAKER_00: Cost.
• 19:13
  SPEAKER_00: You pay for the API.
• 19:15
  SPEAKER_00: If you use Claude Fable 5 via your Anthropic key, you pay Anthropics rates,$10,$50, but you avoid any extra subscription fees or middleman rates.
• 19:26
  SPEAKER_00: If you prefer budget, you can switch to a cheaper model or even a local one with no per token cost, since Kline supports local models too.
• 19:34
  SPEAKER_00: Review Ease.
• 19:35
  SPEAKER_00: Klein's workflow is designed for reviewability.
• 19:38
  SPEAKER_00: Every change is staged, every command and diff is shown, and checkpoints let you undo anything instantly.
• 19:44
  SPEAKER_00: It basically requires an enter to confirm each step, which is slow but safe.
• 19:49
  SPEAKER_00: You can also export a full log of the session for auditing.
• 19:53
  SPEAKER_00: Roo Code, open source VS Code Extension.
• 19:56
  SPEAKER_00: Roo code is another open, model agnostic coding assistant, VS Code extension, geared toward Teams.
• 20:03
  SPEAKER_00: It emphasizes pluggable models and workflows.
• 20:05
  SPEAKER_00: Like Klein, Roo lets you pick any model provider by installing a provider plugin.
• 20:10
  SPEAKER_00: The Roo docs explicitly show integration with Anthropic as a provider option.
• 20:15
  SPEAKER_00: In other words, through the Anthropic provider, you could use Fable 5 if you supply your crypto.
• 20:22
  SPEAKER_00: Model, Roo is model agnostic, meaning you install a provider, Anthropic, OpenAI, Google, etc.
• 20:29
  SPEAKER_00: Roo's docs list Anthropic as a provider you can add with your Claude API key.
• 20:34
  SPEAKER_00: It doesn't come with a built-in model, it's a client framework.
• 20:38
  SPEAKER_00: Usage.
• 20:39
  SPEAKER_00: Roo operates inside VS Code.
• 20:41
  SPEAKER_00: It has modes like ask AI to plan a feature or inline suggestions.
• 20:46
  SPEAKER_00: It can understand repository context through extension APIs.
• 20:50
  SPEAKER_00: Control.
• 20:51
  SPEAKER_00: You have to explicitly enable any provider models you want.
• 20:54
  SPEAKER_00: Like Kline, Roo will surface AI-generated edits as normal diffs in your editor.
• 21:00
  SPEAKER_00: You can undo or tweak them before saving.
• 21:02
  SPEAKER_00: Roo also supports specialized modes, for example, focusing on documentation versus code tasks to steer the AI.
• 21:10
  SPEAKER_00: Context.
• 21:11
  SPEAKER_00: Roo can see your workspace.
• 21:13
  SPEAKER_00: It runs in VS Code with full file access.
• 21:16
  SPEAKER_00: It doesn't have a separate memory beyond the current editing context and any conversation you maintain.
• 21:22
  SPEAKER_00: It has a backend that can chain prompts, but long-term memory or persistent agents are not its focus.
• 21:29
  SPEAKER_00: Safety.
• 21:30
  SPEAKER_00: Being open and local means it's reasonably safe.
• 21:33
  SPEAKER_00: Code is not committed anywhere without review.
• 21:36
  SPEAKER_00: You still send prompts to whichever LLM API you choose, though, so sensitive code leaves your computer.
• 21:43
  SPEAKER_00: Cost.
• 21:44
  SPEAKER_00: Rue itself is free.
• 21:46
  SPEAKER_00: Using it with an anthropic model only costs your API usage.
• 21:50
  SPEAKER_00: Rue also advertises using cheaper LLMs or self hosted ones via providers like Olama or LM Studio to cut down costs.
• 22:00
  SPEAKER_00: Ease, Rue offers specialized modes to stay on task, but each change shows up as VS Code edits, so you review them normally.
• 22:08
  SPEAKER_00: It doesn't automatically commit anything to Git without you merging.
• 22:12
  SPEAKER_00: Continue, open source coding agent.
• 22:15
  SPEAKER_00: Continue is an open source VS Code extension and CLI for AI coding.
• 22:19
  SPEAKER_00: It focuses on source-controlled AI checks and integrating with CI pipelines, but it also offers an interactive agent.
• 22:27
  SPEAKER_00: Its published model registry, Continue Hub, shows it supports Anthropic's Claude 4 Sonnet, the Claude 4.6 model in agent mode.
• 22:36
  SPEAKER_00: Notably, no mention of Claude 5.
• 22:39
  SPEAKER_00: In June 2026, Continue still only lists up to Anthropic Claude 4 Sonnet with 200,000 context.
• 22:46
  SPEAKER_00: That means you can't use Fable 5 through Continue unless its docs project are updated.
• 22:52
  SPEAKER_00: Model.
• 22:53
  SPEAKER_00: The registry indicates support for Claude 4.x and presumably OpenAI GPT models out of the box.
• 23:00
  SPEAKER_00: It doesn't yet list Claude Fable 5, so continue agents would run on the older code-centric models.
• 23:06
  SPEAKER_00: Usage.
• 23:07
  SPEAKER_00: Continue has multiple modes, agent, chat, autocomplete, inside VS Code.
• 23:12
  SPEAKER_00: The agent mode can take a GitHub issue or a task and try to code it across the repo.
• 23:17
  SPEAKER_00: The chat mode is for QA about code.
• 23:20
  SPEAKER_00: There's even a CI integration that enforces rules.
• 23:23
  SPEAKER_00: Control.
• 23:24
  SPEAKER_00: As an IDE extension, suggestions and changes appear in the editor.
• 23:28
  SPEAKER_00: You must approve edits.
• 23:30
  SPEAKER_00: Continue won't silently commit to your repo.
• 23:33
  SPEAKER_00: It also integrates with GitHub, so you can push tasks back as issues slash PRs for review.
• 23:39
  SPEAKER_00: Context.
• 23:40
  SPEAKER_00: Continue knows the repository state.
• 23:42
  SPEAKER_00: It can attach to a GitHub repo.
• 23:44
  SPEAKER_00: Each agent session is a stateful conversation, but there's no published info about long-term memory or persistent rules files.
• 23:52
  SPEAKER_00: It does have a concept of templates and contexts via its hub.
• 23:56
  SPEAKER_00: Safety.
• 23:57
  SPEAKER_00: Source code stays in your session.
• 23:58
  SPEAKER_00: Continuous agent actions require you to accept them.
• 24:02
  SPEAKER_00: Its CI-focused design suggests you can enforce that only reviewed changes merge.
• 24:07
  SPEAKER_00: Cost.
• 24:07
  SPEAKER_00: Continuous free, Apache 2.0.
• 24:10
  SPEAKER_00: It supports whichever LLM APIs you configure.
• 24:13
  SPEAKER_00: So if you happen to wire in Claude Fable 5, you'd pay Anthropics rates.
• 24:18
  SPEAKER_00: But out of the box, it likely uses GPT or Claude 4.
• 24:22
  SPEAKER_00: Review Ease.
• 24:23
  SPEAKER_00: Continue logs every change.
• 24:25
  SPEAKER_00: It also emphasizes creating AI checks, essentially unit tests or linters in CI.
• 24:30
  SPEAKER_00: You can tag any suggestion to also become a code review comment.
• 24:34
  SPEAKER_00: Undoing is just normal Git rollback.
• 24:36
  SPEAKER_00: Devon Cognition AI.
• 24:39
  SPEAKER_00: Devon is a commercial AI software engineer built by Cognition.ai.
• 24:44
  SPEAKER_00: Unlike the other tools, Devon is not just a harness around a public LLM.
• 24:49
  SPEAKER_00: It's a full agent product with its own AI backend, likely a cognition model optimized for code.
• 24:56
  SPEAKER_00: We don't know exactly what model Devon uses, anthropic or custom.
• 25:00
  SPEAKER_00: But Cognition claims Devon exhibits advanced planning and memory beyond typical LLM agents.
• 25:07
  SPEAKER_00: For instance, their blog says Devon can recall relevant context at every step and learn over time.
• 25:13
  SPEAKER_00: In benchmarks, Devon vastly outperformed prior models on open source bug fixing, SWE Bench.
• 25:20
  SPEAKER_00: Model private.
• 25:22
  SPEAKER_00: It's not something you install or configure.
• 25:24
  SPEAKER_00: It's a hosted service.
• 25:26
  SPEAKER_00: Cognition has not branded Devon as a Claude equivalent.
• 25:29
  SPEAKER_00: It's its own LLM or ensemble.
• 25:32
  SPEAKER_00: The company's Cognition AI lab models.
• 25:35
  SPEAKER_00: So from the perspective of Claude Fable 5, Devon is a peer product, not a place to run Claude.
• 25:42
  SPEAKER_00: Usage.
• 25:43
  SPEAKER_00: Devon is intended for large engineering teams.
• 25:46
  SPEAKER_00: It connects to tools like Slack, Jira, GitHub, etc.
• 25:49
  SPEAKER_00: So you can feed it tasks through those channels.
• 25:52
  SPEAKER_00: It operates over hours or days to execute complex tickets.
• 25:56
  SPEAKER_00: Control.
• 25:57
  SPEAKER_00: Because Devon is a managed agent, you interact with it via chat or task tickets.
• 26:02
  SPEAKER_00: It reports progress and solicits feedback.
• 26:05
  SPEAKER_00: End results code changes come back into GitHub or your editor to review.
• 26:09
  SPEAKER_00: You retain ultimate approval of anything it merges.
• 26:13
  SPEAKER_00: Context.
• 26:14
  SPEAKER_00: Devon's key selling point is powerful memory and planning.
• 26:17
  SPEAKER_00: It can recall and use project context at each step, and it learns from feedback.
• 26:22
  SPEAKER_00: This suggests an on-demand memory system far richer than a simple prompt window.
• 26:27
  SPEAKER_00: Safety.
• 26:28
  SPEAKER_00: It runs in a sandboxed cloud environment with tools, shell, browser, etc., that a coder would use.
• 26:34
  SPEAKER_00: Cognition likely has its own controls around what tasks Devon can attempt.
• 26:38
  SPEAKER_00: As a black box SaaS, you must trust Cognition's policies, but merges happen only when approved.
• 26:44
  SPEAKER_00: Cost.
• 26:45
  SPEAKER_00: Devon is a premium product targeted for enterprises.
• 26:48
  SPEAKER_00: Pricing isn't public, but presumably it's on par with other enterprise coding AI.
• 26:53
  SPEAKER_00: The cost of the underlying LLM calls is bundled into the service.
• 26:57
  SPEAKER_00: Review ease.
• 26:58
  SPEAKER_00: Work is done via real GitHub issues and PRs.
• 27:02
  SPEAKER_00: Devon's performance is impressive, around 13 to 14% success on tricky real-world issues.
• 27:08
  SPEAKER_00: But like any AI, it isn't perfect.
• 27:10
  SPEAKER_00: If Devon is available to you, it's one stop, but you're locked into Cognition System.
• 27:15
  SPEAKER_00: Open source terminal agents.
• 27:17
  SPEAKER_00: There are a number of open source coding agents you can run in a terminal, many of which can be pointed at a Claude API.
• 27:23
  SPEAKER_00: For example, the CLI tool OpenAGent advertises itself as an open source alternative to Claud Code.
• 27:30
  SPEAKER_00: It lets you use a Cloud Max subscription or other models from the terminal.
• 27:34
  SPEAKER_00: Another is Claw Code Agent, a Python re-implementation of Claude Code's ideas.
• 27:41
  SPEAKER_00: And there are frameworks like AutoGPT or Langchain that people adapt for coding tasks.
• 27:48
  SPEAKER_00: Models.
• 27:49
  SPEAKER_00: With BYOK, most of these let you use Claude.
• 28:00
  SPEAKER_00: So if your copilot or Claude subscription includes Fable 5, you could theoretically hook it up to OpenAgent.
• 28:08
  SPEAKER_00: In practice, many OpenAGents only hard code up to Opus 4X, like one framework had Sonnet support, but might be updated.
• 28:17
  SPEAKER_00: Usage.
• 28:18
  SPEAKER_00: These run entirely in your terminal.
• 28:21
  SPEAKER_00: You type high-level commands like OpenAGENT Plan and the agent will loop.
• 28:25
  SPEAKER_00: Reading files, writing code, running commands.
• 28:28
  SPEAKER_00: It's a more DIY setup, without a polished UI.
• 28:32
  SPEAKER_00: Control.
• 28:33
  SPEAKER_00: Usually you still approve changes, each diff is printed or opened in an editor for review.
• 28:38
  SPEAKER_00: But some experimental agents have an auto-commit mode, use with caution.
• 28:43
  SPEAKER_00: Checkpoints or git stashes are your friend.
• 28:46
  SPEAKER_00: Context.
• 28:46
  SPEAKER_00: Terminal agents often reload the workspace and chat history each turn.
• 28:51
  SPEAKER_00: If long context is needed, some maintain a rolling prompt history.
• 28:55
  SPEAKER_00: But memory is in deep by default.
• 28:57
  SPEAKER_00: It's up to the tool.
• 28:58
  SPEAKER_00: You might set it to carry on long GPT chats or not.
• 29:02
  SPEAKER_00: Safety.
• 29:02
  SPEAKER_00: High risk if set to auto-run, safer if locked down to review all progress.
• 29:07
  SPEAKER_00: Since you control them locally, your code doesn't leave your machine except via the API to Claude, unless the agent fetches from the web.
• 29:15
  SPEAKER_00: Cost.
• 29:15
  SPEAKER_00: You'll be paying Claude's API.
• 29:17
  SPEAKER_00: Many open agents encourage local models, like Llama derivatives, as cheaper alternatives.
• 29:23
  SPEAKER_00: For Claude Fable 5, you incur the normal$10-50 token cost on every query.
• 29:29
  SPEAKER_00: Review ease.
• 29:30
  SPEAKER_00: This varies.
• 29:31
  SPEAKER_00: Tools like OpenAGent have Git integration built in.
• 29:34
  SPEAKER_00: Others may just rely on you using Git manually.
• 29:38
  SPEAKER_00: All changes are in your local repo, so normal review applies.
• 29:41
  SPEAKER_00: If broken, just git reset.
• 29:44
  SPEAKER_00: Scenario-based comparison.
• 29:46
  SPEAKER_00: Let's walk through common coding scenarios and see which harnesses shine for each with Claude Fable 5, or an equivalent model under the hood.
• 29:53
  SPEAKER_00: Building a new feature across many files.
• 29:56
  SPEAKER_00: This demands large context and planning.
• 29:58
  SPEAKER_00: The top harnesses here are Claude Code with its plan mode and cursor with its agent mode.
• 30:03
  SPEAKER_00: Both can keep track of multi-file changes and iterate.
• 30:07
  SPEAKER_00: Kline local agent also fits.
• 30:09
  SPEAKER_00: You can say implement feature X and it will map out steps, running code and tests.
• 30:14
  SPEAKER_00: Open source terminal agents can do it too, but you'll be manually monitoring.
• 30:19
  SPEAKER_00: Windsurf's Cascade could do it, but recall Anthropic's limited support.
• 30:23
  SPEAKER_00: However, its own SWE agent might attempt it.
• 30:27
  SPEAKER_00: Copilot RegularChat really struggles with bank plans.
• 30:30
  SPEAKER_00: Best, IDE integrated agents with memory.
• 30:33
  SPEAKER_00: Claude code or cursor.
• 30:35
  SPEAKER_00: Debugging a production bug.
• 30:38
  SPEAKER_00: Here you want quick iteration with shell access.
• 30:42
  SPEAKER_00: Klein and Claude Code win because they let Claude run debugging commands and inspect logs directly.
• 30:49
  SPEAKER_00: You can say, fix this stack tree, and it can grep logs, run tests, and try fixes.
• 30:55
  SPEAKER_00: Windsurf's agent is less workflow focused on one-off bugs.
• 31:00
  SPEAKER_00: Copilot Chat is decent at explaining code, but without terminal, it can only guess.
• 31:05
  SPEAKER_00: Continue could do this by opening an issue and walking through it.
• 31:09
  SPEAKER_00: Best, terminal-capable agents like Klein or Claude Code.
• 31:14
  SPEAKER_00: Refactoring a large code base.
• 31:16
  SPEAKER_00: Similar to the feature case, but riskier.
• 31:19
  SPEAKER_00: You need context of the whole code and careful staging.
• 31:23
  SPEAKER_00: Again, Claude Code and Cursor are well suited because they can plan batch changes.
• 31:28
  SPEAKER_00: They also let you commit piecewise.
• 31:31
  SPEAKER_00: An agent like Devon, if it were applied here, has shown strength at large refactors.
• 31:36
  SPEAKER_00: See SWE bench results, though that was bug fixes.
• 31:39
  SPEAKER_00: Clime could do it locally.
• 31:41
  SPEAKER_00: Windsurf's SWE model might attempt big refactor but had limited clawed access.
• 31:46
  SPEAKER_00: Best Hull Environment.
• 31:48
  SPEAKER_00: Claude code or cursor so you can confirm each chunk.
• 31:52
  SPEAKER_00: Writing and updating tests.
• 31:54
  SPEAKER_00: You need the agent to generate code and then run tests.
• 31:57
  SPEAKER_00: Tools with execution access stand out.
• 32:00
  SPEAKER_00: Claud Code and Kline can literally run the test suite and see failures, then update code.
• 32:06
  SPEAKER_00: Windsurf Cursor can suggest tests, but can't execute them internally.
• 32:11
  SPEAKER_00: You copy them back and run.
• 32:13
  SPEAKER_00: Copilot chat can only output test code.
• 32:15
  SPEAKER_00: You run it manually.
• 32:17
  SPEAKER_00: So agents in your IDE terminal are best.
• 32:20
  SPEAKER_00: Best agents with terminal, e.g.
• 32:22
  SPEAKER_00: Cloud Code, Kline.
• 32:23
  SPEAKER_00: Working with unfamiliar frameworks, the model needs to research or reason about new APIs.
• 32:29
  SPEAKER_00: Agents with document browsing help.
• 32:31
  SPEAKER_00: Klein can even open a browser to fetch docs or examples.
• 32:35
  SPEAKER_00: Continue and Devon might look things up in the cloud.
• 32:38
  SPEAKER_00: Truly offline tools can't fetch new info except their training.
• 32:42
  SPEAKER_00: Best, agents that allow web access, client with browser or Devon, which can fetch articles on its own, or that have large knowledge corpora.
• 32:50
  SPEAKER_00: Reading logs and terminal output.
• 32:53
  SPEAKER_00: Agents that can see raw logs and then act on them are needed.
• 32:56
  SPEAKER_00: Klein can show terminal output in the prompt using at output.txt for instance.
• 33:02
  SPEAKER_00: Claude code can also pipe output to the model.
• 33:04
  SPEAKER_00: Cursor WindServe have more of a GUI focus and don't naturally ingest logs.
• 33:09
  SPEAKER_00: Copilot chat can take a log snippet as input, so it can try diagnosing, but it can't run log producing commands itself.
• 33:17
  SPEAKER_00: Best, terminal retaining agents, client, claud code, open agent that let you copy paste or pipe console output into the AI's prompt.
• 33:26
  SPEAKER_00: Creating GitHub issues and PRs.
• 33:28
  SPEAKER_00: Integration is key.
• 33:30
  SPEAKER_00: Cursor explicitly supports working with GitHub Linear, creating issues or linking to them.
• 33:35
  SPEAKER_00: Continue and Devon also connect to GitHub Issues as their interface.
• 33:39
  SPEAKER_00: Clawed code can make a patch and push it to the remote, or one can instruct it in the terminal.
• 33:45
  SPEAKER_00: Copilot chat can generate PR text and code, but you have to copy it.
• 33:50
  SPEAKER_00: Best, tools already built around GitHub.
• 33:53
  SPEAKER_00: Cursor continue devon enabled with integrations for seamless workflow.
• 33:58
  SPEAKER_00: Reviewing code written by another AI agent.
• 34:01
  SPEAKER_00: This is more of a human task, but an AI agent could help review for you.
• 34:05
  SPEAKER_00: Any chat interface works here.
• 34:07
  SPEAKER_00: Copilot chat or cursor's chat would allow you to paste code and ask questions.
• 34:13
  SPEAKER_00: An agent like Klein or Claude Code could open diffs and ask the model to examine them.
• 34:18
  SPEAKER_00: But importantly, you'll be manually verifying.
• 34:21
  SPEAKER_00: There's no harness that automates this fully yet, since review is inherently a human decision.
• 34:27
  SPEAKER_00: Tools that emphasize traceability, like Klein's logs, make human review easier.
• 34:32
  SPEAKER_00: Migrating between library slash framework versions.
• 34:35
  SPEAKER_00: This is a mix of planning and code overhaul.
• 34:38
  SPEAKER_00: It's similar to a big refactor, require understanding of both old and new APIs.
• 34:44
  SPEAKER_00: Agents with wide knowledge, Fable 5 likely trained on lots of ML code, plus memory help.
• 34:50
  SPEAKER_00: Claude code or cursor can plan a migration step by step.
• 34:53
  SPEAKER_00: They also let you test each step via run commands.
• 34:57
  SPEAKER_00: Windsurf and Devon, if available, could attempt migrations because they did well on complex engineering tasks.
• 35:03
  SPEAKER_00: Best.
• 35:03
  SPEAKER_00: The end-to-end agenc systems, Claude Code, Cursor, Devon if used, for multi-step changes.
• 35:10
  SPEAKER_00: Running semi-autonomous work for 30 to 90 minutes.
• 35:13
  SPEAKER_00: This stresses session stability.
• 35:15
  SPEAKER_00: Some tools time out.
• 35:17
  SPEAKER_00: A browser chat might have a short context limit or time budget.
• 35:20
  SPEAKER_00: Clawed code and advertises multi-hour sessions.
• 35:23
  SPEAKER_00: With proper memory, it can work for days at a time on a project.
• 35:27
  SPEAKER_00: Devon reportedly works independently for hours.
• 35:31
  SPEAKER_00: Klein can also run in the background for long tasks as long as your machine is on.
• 35:36
  SPEAKER_00: Cursor agent sessions can span multiple queries in the same window.
• 35:40
  SPEAKER_00: Copilot chat and most simple chatbots cannot sustain a 90-minute uninterrupted session.
• 35:45
  SPEAKER_00: Best.
• 35:46
  SPEAKER_00: Agents designed for longer sessions.
• 35:48
  SPEAKER_00: Claude Code, Devon, Klein.
• 35:51
  SPEAKER_00: Safety and control.
• 35:52
  SPEAKER_00: When letting an AI loose on real code, SafetyNets matter.
• 35:56
  SPEAKER_00: Here's how these tools compare in risk management and user control.
• 36:00
  SPEAKER_00: Permissions.
• 36:01
  SPEAKER_00: Some agents use a principle of least power.
• 36:03
  SPEAKER_00: Klein, Rue, and Claude Code act only when you allow.
• 36:07
  SPEAKER_00: By contrast, an auto agent mode, if enabled, can apply multiple commits without asking.
• 36:12
  SPEAKER_00: High risk if not watched.
• 36:13
  SPEAKER_00: Claude Code CLI always requires a final confirm.
• 36:17
  SPEAKER_00: Windsurf and Cursor only apply changes you accept in the editor.
• 36:21
  SPEAKER_00: Rollback.
• 36:22
  SPEAKER_00: Klein has built-in checkpoints, so you can instantly revert the entire project to a previous state.
• 36:28
  SPEAKER_00: Most other tools rely on Git for undo.
• 36:31
  SPEAKER_00: Cursor and continue show diffs that you can undo locally.
• 36:34
  SPEAKER_00: Input, output safety.
• 36:37
  SPEAKER_00: Anthropics models have strong content filters.
• 36:40
  SPEAKER_00: For example, Fable 5 will switch to a safer model if a query is flagged as a hacking or cyber weapons prompt.
• 36:47
  SPEAKER_00: So driving it through any of these tools inherits those safeguards.
• 36:52
  SPEAKER_00: The tools themselves add another layer, e.g., safe mode in clawed code or blocking certain shell commands.
• 36:59
  SPEAKER_00: However, any agent that runs code is powerful.
• 37:03
  SPEAKER_00: You should never run it unsupervised on sensitive production environments.
• 37:07
  SPEAKER_00: Transparency.
• 37:09
  SPEAKER_00: Closed systems hide prompts.
• 37:11
  SPEAKER_00: Klein and Roo emphasize transparency.
• 37:14
  SPEAKER_00: You see exactly what prompt the model dot and every diff it produced.
• 37:18
  SPEAKER_00: In closed products, cursor, windsurf, you see suggestions, but not the exact hidden prompting logic.
• 37:25
  SPEAKER_00: For auditing, open source tools win.
• 37:28
  SPEAKER_00: In summary, open source or self-hosted harnesses, Klein, Roo, OpenAG, give you the most control and audit trail, making them safest for real reps.
• 37:38
  SPEAKER_00: Proprietary tools, Clawed Code, Cursor, Windsurf, can be safe if used carefully, since you still approve all code in your IDE, but you are handing review to a somewhat opaque cloud system.
• 37:50
  SPEAKER_00: GitHub's Anthropic Agent gives heavy enterprise controls.
• 37:54
  SPEAKER_00: It sits behind corporate copilot admin.
• 37:57
  SPEAKER_00: But you're trusting GitHub and Anthropics filters.
• 38:00
  SPEAKER_00: Cost and practicality.
• 38:02
  SPEAKER_00: Finally, let's weigh cost and usability.
• 38:04
  SPEAKER_00: Daily use.
• 38:05
  SPEAKER_00: For day-to-day code help, many developers use copilot or cursor chat modes, or even chat GPT, because they feel quick and interactive.
• 38:14
  SPEAKER_00: But those aren't as powerful for deep tasks.
• 38:16
  SPEAKER_00: If you want to build features, you don't want to keep switching between a browser and your code.
• 38:21
  SPEAKER_00: Tools like Claude Code in your editor or Kline in your IGE embed the AI in the actual coding environment, which feels more practical despite the learning curve.
• 38:30
  SPEAKER_00: Heavy agentic work?
• 38:32
  SPEAKER_00: For big projects, platforms like Windsurf Cursor or enterprise solutions like Devon really shine, but they require onboarding, company approval, and cost.
• 38:42
  SPEAKER_00: Open source CLI agents or Claude Code, though, are surprisingly capable for solo or startup needs, since you can self-host.
• 38:50
  SPEAKER_00: They are free to install, you only pay the LLM API fees.
• 38:54
  SPEAKER_00: Occasional tasks.
• 38:55
  SPEAKER_00: If you only occasionally want to offload a coding task, a simpler chat, compilot chat, chat GPT, might suffice because you don't need the overhead of an agent session.
• 39:05
  SPEAKER_00: But beware, chat won't manage long tasks or keep context.
• 39:09
  SPEAKER_00: Enterprise needs.
• 39:10
  SPEAKER_00: Larger companies often prefer managed environments with audit controls.
• 39:14
  SPEAKER_00: They might choose Windsurf or Devon, Cognition for big teams, even if Anthropic limits model access.
• 39:20
  SPEAKER_00: Those products bundle agent capability and dashboards.
• 39:23
  SPEAKER_00: Alternatively, they might permit personal agents, like Claude Code with policy rules, but insist on code review pipelines when cost matters.
• 39:32
  SPEAKER_00: If budget is tight, lean on the free BYLK hybrid route.
• 39:37
  SPEAKER_00: For example, running the local client with GPT 3.5 via OpenRouter is very cheap.
• 39:44
  SPEAKER_00: Even using Claude via rope with careful prompt caching, 90% discount for repeated context drastically lowers costs.
• 39:52
  SPEAKER_00: In other words, you can tailor the harness to your budget, maybe run a cheaper Claude 4 model on small tasks, and only kick in Fable 5 for the most critical, high-value jobs.
• 40:04
  SPEAKER_00: Verdict Best overall hardest for Claude.
• 40:08
  SPEAKER_00: Many experts would pick Anthropic's own Claude Code or its Cloud IDE when you truly need heavy agentic power.
• 40:14
  SPEAKER_00: It's built and supported by the model's creators, can use Fable 5 today, and is designed for software projects.
• 40:21
  SPEAKER_00: In practice, however, tools like Cursor can also unleash Fable 5 power in a slick UI.
• 40:28
  SPEAKER_00: Best for solo developers, probably Klein or Ruecode.
• 40:32
  SPEAKER_00: They're free, open source, running locally for transparency and no extras.
• 40:37
  SPEAKER_00: You supply your Claud key, so you automatically use any model you have access to, including Fable 5.
• 40:43
  SPEAKER_00: The learning curve is a bit deeper, but you stay in full control and can customize everything.
• 40:49
  SPEAKER_00: Best for startups.
• 40:51
  SPEAKER_00: A mix.
• 40:52
  SPEAKER_00: A startup founder could use Windsurf if the claude access issue is resolved, or cursor for rapid feature building, while also having Klein available for safe local work.
• 41:01
  SPEAKER_00: For quick wins, Copilot Chat plus a manual or similar covers QA, but for real feature work, an agent harness is required.
• 41:09
  SPEAKER_00: Best for large codebases, agents that keep full context, cloud code in its multi-agent mode, or enterprise platforms like Devon.
• 41:17
  SPEAKER_00: These can manage thousands of files and complex architecture.
• 41:21
  SPEAKER_00: They also integrate project memory or knowledge bases so the model doesn't keep repeating itself.
• 41:26
  SPEAKER_00: Best for safe enterprise work.
• 41:28
  SPEAKER_00: Tools that emphasize compliance, like continue, with CI checks, or Kline, open auditable.
• 41:34
  SPEAKER_00: Alternatively, GitHub Copilots Cloud Agent in a lockdown preview can follow corporate policy.
• 41:40
  SPEAKER_00: In any case, requiring human review of every change is key.
• 41:44
  SPEAKER_00: Best open source API option.
• 41:47
  SPEAKER_00: Clearly Klein.
• 41:48
  SPEAKER_00: It is explicitly open and supports any provider you plug in with a battle-tested local workflow.
• 41:54
  SPEAKER_00: OpenAGent is another strong contender in CLI form.
• 41:57
  SPEAKER_00: Both let you leverage Claude Fable 5 with your key without vendor lock-in.
• 42:02
  SPEAKER_00: Best when cost is critical, use cheaper or self-hosted solutions.
• 42:06
  SPEAKER_00: That means default to systems using Claude 4 or Open LLMs or run agents locally.
• 42:12
  SPEAKER_00: For example, use cursor sue models or run Claude on lower tiers except when Fable's extra power is justified.
• 42:20
  SPEAKER_00: Best for autonomy, if you want the AI to run itself on a task with minimal guidance, Claude Code or Devon are champions.
• 42:27
  SPEAKER_00: They can plan and execute ongoing tasks.
• 42:30
  SPEAKER_00: Open source agents, like OpenAGENT, also support autonomy, but you must conceptually turn the key each step.
• 42:38
  SPEAKER_00: For fully hands-off operation, dedicated platforms are a bit ahead.
• 42:43
  SPEAKER_00: Podcast-friendly closing.
• 42:45
  SPEAKER_00: In the end, the lesson is the smartest model isn't automatically the best coder.
• 42:50
  SPEAKER_00: You need the right coding harness.
• 42:51
  SPEAKER_00: A powerful Clogbrain needs good eyes, the ability to read the whole project, hands, ability to edit files, run tests, memory, to recall past steps, and breaks to stop before disaster.
• 43:05
  SPEAKER_00: Whether it's in Claude Code's terminal loop, cursor's IDE agent, or a local CLI like Klein, the entire system defines what the AI can actually accomplish.
• 43:15
  SPEAKER_00: As one anthropic exec put it, we're moving beyond static chatbots toward true AI teammates.
• 43:22
  SPEAKER_00: The best system will give that AI teammate what it needs to be a reliable engineer, not just a fast talker.
• 43:29
  SPEAKER_00: All links to sources are available in the text version of this article.
• 43:33
  SPEAKER_00: You can find the full article at easycoding.toolslash blog.
• 43:37
  SPEAKER_00: Thanks for listening.
• 43:39
  SPEAKER_00: For more practical AI coding guides, tool comparisons, and resources for builders, visit easycoding.tools.
• 43:46
  SPEAKER_00: And if it's easier to remember, just go to aibuilds.it.
• 43:50
  SPEAKER_00: It redirects to easycoding.tools.