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[161.38.201.120]) by mx.google.com with UTF8SMTPS id d9443c01a7336-2baf1e9d740si95763505ad.122.2026.05.09.08.42.52 for (version=TLS1_2 cipher=ECDHE-ECDSA-AES128-GCM-SHA256 bits=128/128); Sat, 09 May 2026 08:42:53 -0700 (PDT) Received-SPF: pass (google.com: domain of bounce+a6cce7.4d1858-tjphuhs=gmail.com@mg2.substack.com designates 161.38.201.120 as permitted sender) client-ip=161.38.201.120; Authentication-Results: mx.google.com; dkim=pass header.i=@mg2.substack.com header.s=mailo header.b=iRWYw1CJ; dkim=pass header.i=@mailgun.org header.s=mg header.b="Jvi/9fIu"; spf=pass (google.com: domain of bounce+a6cce7.4d1858-tjphuhs=gmail.com@mg2.substack.com designates 161.38.201.120 as permitted sender) smtp.mailfrom="bounce+a6cce7.4d1858-tjphuhs=gmail.com@mg2.substack.com"; dmarc=pass (p=REJECT sp=REJECT dis=NONE) header.from=substack.com DKIM-Signature: a=rsa-sha256; v=1; c=relaxed/relaxed; d=mg2.substack.com; q=dns/txt; s=mailo; t=1778341372; x=1778348572; h=List-Unsubscribe-Post: List-Unsubscribe: List-Post: List-Id: List-Archive: List-Owner: Reply-To: In-Reply-To: References: Sender: Sender: Date: Message-Id: To: To: From: From: Subject: Subject: Content-Type: Mime-Version: X-Feedback-Id; bh=IJwHj/YMPr0SD34Mg2lL3xl3gMWh5Z8g6yM/a/N0DSI=; b=iRWYw1CJFNh2oGrUxyHPhH/rITp8uL0y2c/iK70/S8jMIVz1LOV0U0RsSVspHt+o1tdF5RJ4rY9/ZIF5mS8tvDMUlkEPtZvwn4dCNdEKF5znuaW07IG2RQvy/IbL9F+JsR63nbvgtypg6K6kGwM6wczdpkXtOIdLEH9d/mwKOtI= DKIM-Signature: a=rsa-sha256; v=1; c=relaxed/relaxed; d=mailgun.org; q=dns/txt; s=mg; t=1778341372; x=1778348572; h=List-Unsubscribe-Post: List-Unsubscribe: List-Post: List-Id: List-Archive: List-Owner: Reply-To: In-Reply-To: References: Sender: Sender: Date: Message-Id: To: To: From: From: Subject: Subject: Content-Type: Mime-Version: X-Feedback-Id; bh=IJwHj/YMPr0SD34Mg2lL3xl3gMWh5Z8g6yM/a/N0DSI=; b=Jvi/9fIuCcwrVXNepldiE89+k9NA14e/hYLCXad7XiPHrBxNe/JjNAO5NxQqyH1vq8K9mnaNTxtNjRh+YoZOPInQpMQairl4Z+SkjUY1ambyNb8CY6QooGZaz2Gp2C4/EL1jcBfTwHW52J7XkPdbs6sIuaS2rTXKb1u6b/WqVZM= X-Mailgun-Sid: WyIxODZiYSIsInRqcGh1aHNAZ21haWwuY29tIiwiNGQxODU4Il0= X-Feedback-Id: postmaster@mg2.substack.com:post:5a78b6d5cd16820001f2a9d4:mailgun Received: by a76155abc52fbdd15fa35af3d7fb790b157fe7a898bc70f2a065224f9198e3f3 with HTTP id 69ff55fc7723a59b5c39d6c1; Sat, 09 May 2026 15:42:52 GMT X-Mailgun-Sending-Ip: 161.38.201.120 Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="18a2009678e8061e0e1a78ff0fe8c97e18ad28e97d04b4352e471ca4632e" Subject: EP214: Claude Code vs. OpenClaw: 5 Design Dimensions From: ByteByteGo To: tjphuhs@gmail.com X-Mailgun-Tag: post X-Mailgun-Track-Clicks: false Message-Id: <20260509153110.3.99187a8449f89683@mg2.substack.com> Date: Sat, 9 May 2026 15:31:10 +0000 Feedback-Id: post-196927802:cat-post:pub-817132:substack Sender: ByteByteGo References: In-Reply-To: Reply-To: ByteByteGo List-Owner: List-Url: List-Archive: List-Id: List-Post: List-Unsubscribe: List-Unsubscribe-Post: List-Unsubscribe=One-Click X-Mailgun-Variables: {"category": "post", "email_generated_at": "1778341372307", "is_freemail": "true", "post_audience": "everyone", "post_id": "196927802", "post_type": "newsletter", "pub_community_enabled": "true", "publication_id": "817132", "subdomain": "bytebytego", "user_id": "273622398"} --18a2009678e8061e0e1a78ff0fe8c97e18ad28e97d04b4352e471ca4632e Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: quoted-printable View this post on the web at https://blog.bytebytego.com/p/ep214-claude-cod= e-vs-openclaw-5-design =E2=9C=82=EF=B8=8F Cut your QA cycles down to minutes with QA Wolf (Sponsor= ed) [ https://substack.com/redirect/c50084d0-c753-4171-ad65-1a850f2ba372?j= =3DeyJ1IjoiNGl3b2U2In0.sVDxRtmZ85v8kfdamY0krRXGMy3p768BWtuZifRB-Zs ] If slow QA processes bottleneck you or your software engineering team and y= ou=E2=80=99re releasing slower because of it =E2=80=94 you need to check ou= t QA Wolf. QA Wolf=E2=80=99s AI-native service supports web and mobile apps, deliverin= g 80% automated test coverage in weeks [ https://substack.com/redirect/0ad1= 34e3-6493-4bc6-b04f-6e1b7113d910?j=3DeyJ1IjoiNGl3b2U2In0.sVDxRtmZ85v8kfdamY= 0krRXGMy3p768BWtuZifRB-Zs ] and helping teams ship 5x faster by reducing QA= cycles to minutes. QA Wolf [ https://substack.com/redirect/6b83d939-c33b-4c70-89b2-dfb0d3290a9= 9?j=3DeyJ1IjoiNGl3b2U2In0.sVDxRtmZ85v8kfdamY0krRXGMy3p768BWtuZifRB-Zs ] tak= es testing off your plate. They can get you: Unlimited parallel test runs for mobile and web apps 24-hour maintenance and on-demand test creation Human-verified bug reports sent directly to your team Zero flakes guarantee The benefit? No more manual E2E testing. No more slow QA cycles. No more bu= gs reaching production. With QA Wolf, Drata=E2=80=99s team of 80+ engineers [ https://substack.com/= redirect/b960f247-2bac-40e3-ba50-33cc7ea83e11?j=3DeyJ1IjoiNGl3b2U2In0.sVDxR= tmZ85v8kfdamY0krRXGMy3p768BWtuZifRB-Zs ] achieved 4x more test cases and 86= % faster QA cycles. This week=E2=80=99s system design refresher: Claude Code vs. OpenClaw: 5 Design Dimensions Become an AI Engineer | Enrollment Ends Soon How AI Fakes a Human in 5 Steps How do you know if your AI app actually works? Why Does Git Revert Cause Conflicts? Claude Code vs. OpenClaw: 5 Design Dimensions Claude Code terminates after every task. OpenClaw never sleeps. Both are hi= ghly capable, but they have key architectural differences. System Scope Claude Code is a short-lived process. You launch it, it runs, it exits. Ope= nClaw is a long-running background daemon with a Gateway that holds open We= bSocket connections to apps like Discord, Slack, and WhatsApp. Agent Runtime Claude Code uses a single async query loop: think, tool call, observe, repe= at. OpenClaw uses per-session queues, where the Gateway routes RPCs into se= parate queues. Extension Architecture Claude Code supports MCP, plug, skill, and hook, all wired into the agent. = OpenClaw uses a manifest-first plugin system. Plugins flow through a centra= l registry before reaching the Agent. Memory=20 Claude Code treats CLAUDE. md as memory. OpenClaw separates MEMORY. md from= daily notes and adds hybrid vector/keyword search across structured sectio= ns. Multi-agent & Routing Claude Code uses a lead-to-subagent pattern. OpenClaw uses a route-and-dele= gate system where inbound channels get routed to dedicated agents that hand= off to shared subagents. Over to you: which pattern do you think is the future of agents? Become an AI Engineer | Enrollment Ends Soon Our 6th cohort of Becoming an AI Engineer starts in about a week. This is a= live, cohort-based course created in collaboration with best-selling autho= r Ali Aminian and published by ByteByteGo. Here=E2=80=99s what makes this cohort special: Learn by doing: Build real world AI applications, not just by watching vide= os. Structured, systematic learning path: Follow a carefully designed curriculu= m that takes you step by step, from fundamentals to advanced topics. Live feedback and mentorship: Get direct feedback from instructors and peer= s. Community driven: Learning alone is hard. Learning with a community is easy= ! We are focused on skill building, not just theory or passive learning. Our = goal is for every participant to walk away with a strong foundation for bui= lding AI systems. If you want to start learning AI from scratch, this is the perfect platform= for you to begin. How AI Fakes a Human in 5 Steps One selfie in, one fake video out. Here's how deepfakes work at a high leve= l. The diagram below shows the full pipeline that turns a reference image like= selfie, a voice clip, and a prompt into a fake video. Step 1: Prompt Refinement. The text prompt gets cleaned, augmented with ext= ra detail, and paired with a negative prompt to suppress unwanted artifacts= like distorted hands. Step 2: Reference Image Prep. A single selfie of the target is passed throu= gh a VAE encoder, a neural network that compresses images into a compact la= tent representation. Step 3: Diffusion Inference Engine. Starts from pure noise and runs a diffu= sion-based denoiser, conditioned on the refined prompt, reference latent, a= nd audio to produce clean video latents. A VAE decoder then converts those = latents back into video frames. Step 4: Post-Processing. The raw frames are upscaled to higher resolution, = color-corrected for consistency, screened by an NSFW classifier, and stampe= d with a watermark. Step 5: Multimodal Syncer. Audio is converted to phonemes (the distinct sou= nd units of speech). A lip-sync model aligns mouth movements to those phone= mes. The output is a video of a CEO who never said those words, in a room they n= ever entered. Over to you: What do you look for to figure out if a video's real or made b= y AI? How do you know if your AI app actually works? You evaluate it. But most teams skip this step (or do it wrong) because "ev= al" feels vague. It's not.=20 Every good eval is a 3-step recipe. Step 1: Pick a task. AI systems have different capabilities and dimensions = to evaluate. For LLMs, it can be safety or math capability, in RAGs it can = be grounding and retrieval, Pick one. Step 2: Collect eval data. For every task, gather inputs paired with the ri= ght answer or expected behavior. A safety set pairs risky prompts with "ref= use."=20 Step 3: Develop a grader. How do you decide if the output is good?=20 Use code-based graders (if/else, unit tests) for things with a clear correc= t answer and patch passing unit-tests.=20 Use model-based graders (LLM-as-judge) for subjective tasks like safety. Use human graders for edge cases and anything where nuance matters more tha= n throughput. Most production evals combine all three. Code-based for what's cheap to che= ck. Model-based for scale. Human-based for what matters most. Over to you: what's the hardest thing about your task to grade, and which g= rader type do you use for it? Why Does Git Revert Cause Conflicts? git revert looks straightforward until it throws a conflict. Here's why tha= t happens. What git revert actually does: Unlike reset, a revert doesn=E2=80=99t rewri= te history. Instead, it creates a new commit that undoes the changes from a= n earlier one. This keeps your history clean, traceable, and safe for share= d branches. Why revert conflicts happen: Conflicts appear when a later commit changed t= he same lines as the commit you're trying to undo. Example in the diagram: Commit C2 added a feature Commit C3 changed those same lines Reverting C2 now collides with changes from C3 Git can=E2=80=99t know which version is correct, so a revert conflict is tr= iggered. How to resolve it: 1. Run git revert C2 2. Git pauses when it hits the conflict 3. You manually fix the file 4. Stage it 5. Continue the revert Git then creates a new commit that cleanly undoes C2 while keeping C3 intac= t. Over to you: Have you ever hit a revert conflict at the worst possible mome= nt? How did you resolve it? Unsubscribe https://substack.com/redirect/2/eyJlIjoiaHR0cHM6Ly9ibG9nLmJ5dGV= ieXRlZ28uY29tL2FjdGlvbi9kaXNhYmxlX2VtYWlsP3Rva2VuPWV5SjFjMlZ5WDJsa0lqb3lOek= 0yTWpJek9UZ3NJbkJ2YzNSZmFXUWlPakU1TmpreU56Z3dNaXdpYVdGMElqb3hOemM0TXpReE16Y= 3lMQ0psZUhBaU9qRTRNRGs0Tnpjek56SXNJbWx6Y3lJNkluQjFZaTA0TVRjeE16SWlMQ0p6ZFdJ= aU9pSmthWE5oWW14bFgyVnRZV2xzSW4wLk40T08xQU1HLThURjRxLUZTTkhMWmxSLTRGZ0xtaXh= nZm94NnZLWHp2d2MiLCJwIjoxOTY5Mjc4MDIsInMiOjgxNzEzMiwiZiI6dHJ1ZSwidSI6MjczNj= IyMzk4LCJpYXQiOjE3NzgzNDEzNzIsImV4cCI6MjA5MzkxNzM3MiwiaXNzIjoicHViLTAiLCJzd= WIiOiJsaW5rLXJlZGlyZWN0In0.s6Hos_dmN9DKkfs-UkDYM2GoxRWh1OAd6v61gl8jikw? --18a2009678e8061e0e1a78ff0fe8c97e18ad28e97d04b4352e471ca4632e Content-Type: text/html; charset="utf-8" Content-Transfer-Encoding: quoted-printable = EP214: Claude Code vs. OpenClaw: 5 Design Dimensions3D""
Both are highly capable, but they have key architectu= ral differences.
͏     ­͏     ­&= #847;     ­͏     ­͏    = ; ­͏     ­͏     ­͏ &nbs= p;   ­͏     ­͏     ­&#= 847;     ­͏     ­͏    = ­͏     ­͏     ­͏  = ;   ­͏     ­͏     ­= 47;     ­͏     ­͏     = ­͏     ­͏     ­͏  =   ­͏     ­͏     ­T= 7;     ­͏     ­͏     &= #173;͏     ­͏     ­͏   =   ­͏     ­͏     ­͏= ;     ­͏     ­͏     &#= 173;͏     ­͏     ­͏   &= #8199; ­͏     ­͏     ­͏=     ­͏     ­͏     = 73;͏     ­͏     ­͏   &#= 8199; ­͏     ­͏     ­͏ =     ­͏     ­͏     = 3;͏     ­͏     ­͏   = 199; ­͏     ­͏     ­͏ &= nbsp;   ­͏     ­͏     ­= ;͏     ­͏     ­͏   Q= 99; ­͏     ­͏     ­͏ &n= bsp;   ­͏     ­͏     ­= ͏     ­͏     ­͏   ̳= 9; ­͏     ­͏     ­͏ &nb= sp;   ­͏     ­͏     ­&= #847;     ­͏     ­͏    = ; ­͏     ­͏     ­͏ &nbs= p;   ­͏     ­͏     ­&#= 847;     ­͏     ­͏    = ­͏     ­͏     ­͏  = ;   ­͏     ­͏     ­= 47;     ­͏     ­͏     = ­͏     ­͏     ­͏  =   ­͏     ­͏     ­T= 7;     ­͏     ­͏     &= #173;͏     ­͏     ­͏   =   ­͏     ­͏     ­͏= ;     ­͏     ­͏     &#= 173;͏     ­͏     ­͏   &= #8199; ­͏     ­͏     ­͏=     ­͏     ­͏     = 73;͏     ­͏     ­͏   &#= 8199; ­͏     ­͏     ­͏ =     ­͏     ­͏     = 3;͏     ­͏     ­͏   = 199; ­͏     ­͏     ­͏ &= nbsp;   ­͏     ­͏     ­= ;͏     ­͏     ­͏   Q= 99; ­͏     ­͏     ­͏ &n= bsp;   ­͏     ­͏     ­= ͏     ­͏     ­͏   ̳= 9; ­͏     ­͏     ­͏ &nb= sp;   ­͏     ­͏     ­&= #847;     ­͏     ­͏    = ; ­͏     ­͏     ­͏ &nbs= p;   ­͏     ­͏     ­&#= 847;     ­͏     ­͏    = ­͏     ­͏     ­͏  = ;   ­͏     ­͏     ­= 47;     ­͏     ­͏     = ­͏     ­͏     ­͏  =   ­͏     ­͏     ­T= 7;     ­͏     ­͏     &= #173;͏     ­͏     ­͏   =   ­͏     ­͏     ­͏= ;     ­͏     ­͏     &#= 173;͏     ­͏     ­͏   &= #8199; ­͏     ­͏     ­͏=     ­͏     ­͏     = 73;͏     ­͏     ­͏   &#= 8199; ­͏     ­͏     ­
<= table class=3D"email-body-container" role=3D"presentation" width=3D"100%" b= order=3D"0" cellspacing=3D"0" cellpadding=3D"0">
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EP214: Claude Code vs. OpenClaw= : 5 Design Dimensions

ByteByteGo
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<= /td>
=
<= td height=3D"16" style=3D"font-size:0px;line-height:0;"> 
3D""
=3D""
=3D""
3D""=
READ IN APP3D""
=
 

✂️ Cut y= our QA cycles down to minutes with QA Wolf (Sponsored)

<= td class=3D"content" align=3D"left" width=3D"1456" style=3D"text-align: cen= ter;">3D""

If slow QA processes bottleneck you or your software engineering team= and you’re releasing slower because of it — you need to check = out QA Wolf.

QA Wolf’s AI-native service supports web and mobile apps, delivering 80% automated test coverage in w= eeks and helping teams ship 5x faster by reducing QA cycles to minutes.

QA Wolf takes testing off your plat= e. They can get you:

  • Unlimited parallel test runs = for mobile and web apps

  • 24-hour maintenance and on-demand test creation

  • Human-verified bug reports sent direc= tly to your team

  • = Zero flakes guarantee

The benefit? No more manual= E2E testing. No more slow QA cycles. No more bugs reaching production.

=

With QA Wolf, Drata’s team of 80+ engineers achieved 4x more te= st cases and 86% faster QA cycles.

=

Schedule a demo to learn more

This weekR= 17;s system design refresher:

    Claude Code vs. OpenClaw: 5= Design Dimensions

  • How AI Fakes a Human in 5 Steps

  • How do you know if your AI app= actually works?

  • = Why Does Git Revert Cause Conflicts?

Cl= aude Code vs. OpenClaw: 5 Design Dimensions

Claude Code te= rminates after every task. OpenClaw never sleeps. Both are highly capable, = but they have key architectural differences.

3D"Image"

System Scope
Claude Code is a short-lived p= rocess. You launch it, it runs, it exits. OpenClaw is a long-running backgr= ound daemon with a Gateway that holds open WebSocket connections to apps li= ke Discord, Slack, and WhatsApp.

Agent Runtime=
Claude Code uses a single async query loop: think, tool ca= ll, observe, repeat. OpenClaw uses per-session queues, where the Gateway ro= utes RPCs into separate queues.

Extension Arch= itecture
Claude Code supports MCP, plug, skill, and hook, a= ll wired into the agent. OpenClaw uses a manifest-first plugin system. Plug= ins flow through a central registry before reaching the Agent.

Memory
Claude Code treats CLAUDE. md as m= emory. OpenClaw separates MEMORY. md from daily notes and adds hybrid vecto= r/keyword search across structured sections.

M= ulti-agent & Routing
Claude Code uses a lead-to-subagen= t pattern. OpenClaw uses a route-and-delegate system where inbound channels= get routed to dedicated agents that hand off to shared subagents.

Over to you: which pattern do you think is the future of age= nts?

=

Become an AI Engineer | Enrollment Ends Soon=

Our 6th cohort of Becoming an AI= Engineer starts in about a week. This is a live, cohor= t-based course created in collaboration with best-selling au= thor Ali Aminian and published by ByteByteGo.

Check it out Here

<= a class=3D"image-link" target=3D"_blank" href=3D"https://substack.com/redir= ect/6fba0542-48fc-46e5-8e8b-895632c085a6?j=3DeyJ1IjoiNGl3b2U2In0.sVDxRtmZ85= v8kfdamY0krRXGMy3p768BWtuZifRB-Zs" style=3D"position: relative;flex-directi= on: column;align-items: center;padding: 0;width: auto;height: auto;border: = none;text-decoration: none;display: block;margin: 0;">3D""

Here’s what makes this cohort special:

<= ul style=3D"margin-top: 0;padding: 0;">

  • Learn by doing: Build real world AI applications, not just by= watching videos.

  • Structured, systematic learning path: Follow a carefully designed curricul= um that takes you step by step, from fundamentals to advanced topics.

  • Live feedback and men= torship: Get direct feedback from instructors and peers.

  • Community driven: Learning alone i= s hard. Learning with a community is easy!

    • We are= focused on skill building, not just theory or passive learning. Our goal i= s for every participant to walk away with a strong foundation for building = AI systems.

    If you want to start learning AI from scratch, = this is the perfect platform for you to begin.

    Check it out Here

    How AI Fakes = a Human in 5 Steps

    One selfie in, one fake video out. Here= 's how deepfakes work at a high level.

    The diagram below sh= ows the full pipeline that turns a reference image like selfie, a voice cli= p, and a prompt into a fake video.

    <= figure style=3D"width: 100%;margin: 0 auto;">

    = Step 1: Prompt Refinement. The text prompt gets cleaned, augmented with ext= ra detail, and paired with a negative prompt to suppress unwanted artifacts= like distorted hands.

    Step 2: Reference Image Prep. A sing= le selfie of the target is passed through a VAE encoder, a neural network t= hat compresses images into a compact latent representation.

    Step 3: Diffusion Inference Engine. Starts from pure noise and runs a diff= usion-based denoiser, conditioned on the refined prompt, reference latent, = and audio to produce clean video latents. A VAE decoder then converts those= latents back into video frames.

    Step 4: Post-Processing. T= he raw frames are upscaled to higher resolution, color-corrected for consis= tency, screened by an NSFW classifier, and stamped with a watermark.

    Step 5: Multimodal Syncer. Audio is converted to phonemes (the di= stinct sound units of speech). A lip-sync model aligns mouth movements to t= hose phonemes.

    The output is a video of a CEO who never sai= d those words, in a room they never entered.

    Over to you: W= hat do you look for to figure out if a video's real or made by AI?

    How do you know if your AI app actually works?

    You evaluate it. But most teams skip this step (or do it wrong) beca= use "eval" feels vague. It's not.

    <= figure style=3D"width: 100%;margin: 0 auto;">

    = Every good eval is a 3-step recipe.

    Step 1: Pick a task. AI= systems have different capabilities and dimensions to evaluate. For LLMs, = it can be safety or math capability, in RAGs it can be grounding and retrie= val, Pick one.

    Step 2: Collect eval data. For every task, g= ather inputs paired with the right answer or expected behavior. A safety se= t pairs risky prompts with "refuse."

    Step 3: Develop a gra= der. How do you decide if the output is good?

    • Use code-b= ased graders (if/else, unit tests) for things with a clear correct answer a= nd patch passing unit-tests.

    • Use model-based graders (LLM-as-judge) for subjective tasks li= ke safety.

    • Use hu= man graders for edge cases and anything where nuance matters more than thro= ughput.

    Most production evals combine all three. = Code-based for what's cheap to check. Model-based for scale. Human-based fo= r what matters most.

    Over to you: what's the hardest thing = about your task to grade, and which grader type do you use for it?

    Why Does Git Revert Cause Conflicts?

    = 3D"No

    git revert l= ooks straightforward until it throws a conflict. Here's why that happens.

    • What git revert actually does: Unlike reset, a revert does= n’t rewrite history. Instead, it creates a new commit that undoes the= changes from an earlier one. This keeps your history clean, traceable, and= safe for shared branches.

    • Why revert conflicts happen: Conflicts appear when a later commit= changed the same lines as the commit you're trying to undo.

    <= p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-s= ize: 16px;">Example in the diagram:

    • Commit C2 added a fea= ture

    • Commit C3 ch= anged those same lines

    • Reverting C2 now collides with changes from C3

    Git can’t know which version is correct, so a revert conflict is= triggered.

    • How to resolve it:
      1. R= un git revert C2
      2. Git pauses when it hits the conflict
      3. You manually fix the file
      4. Stage it
      5. Continue the revert

    Git the= n creates a new commit that cleanly undoes C2 while keeping C3 intact.

    <= p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-s= ize: 16px;margin-bottom: 0;">Over to you: Have you ever hit a revert confli= ct at the worst possible moment? How did you resolve it?

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