[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-baz-spec-review-agent-bedrock-agentcore-en":3,"article-related-baz-spec-review-agent-bedrock-agentcore-en":30,"series-industry-b7eb0616-74c8-417f-a62f-7d4c5eeabd0b":83},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":22,"views":26,"created_at":27,"published_at":28,"topic_cluster_id":29},"b7eb0616-74c8-417f-a62f-7d4c5eeabd0b","baz-spec-review-agent-bedrock-agentcore-en","Baz’s review agent turns specs into merge checks","\u003Cp data-speakable=\"summary\">Baz turns specs and UI into automated pull request checks.\u003C\u002Fp>\u003Cp>I've been around enough \u003Ca href=\"\u002Ftag\u002Fcode-review\">code review\u003C\u002Fa> systems to know when one is pretending. Diff-only review feels productive right up until the first time a feature compiles, ships, and still misses the actual product requirement. I’ve seen teams pat themselves on the back because the PR was “clean,” then QA spends the next day clicking through a preview app and finding the thing nobody asked for. That’s the part that always annoyed me: the review process is wired to judge code shape, not product intent.\u003C\u002Fp>\u003Cp>So when I read Baz’s write-up on how they improved \u003Ca href=\"\u002Ftag\u002Fai-agent\">AI agent\u003C\u002Fa> code review accuracy with \u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fmachine-learning\u002Fhow-baz-improved-its-ai-agent-code-review-accuracy-using-amazon-bedrock-agentcore\u002F\">Amazon Bedrock AgentCore\u003C\u002Fa>, it clicked immediately. They’re not trying to make the model sound smarter. They’re trying to make review stop lying. The interesting bit is that they pull in \u003Ca href=\"https:\u002F\u002Fwww.figma.com\u002F\">Figma\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fwww.atlassian.com\u002Fsoftware\u002Fjira\">Jira\u003C\u002Fa>, break the spec into smaller checks, then validate the live implementation in a browser instead of trusting static analysis alone. That’s the right instinct, and honestly, it’s overdue.\u003C\u002Fp>\u003Ch2>Stop reviewing the diff like it tells the whole story\u003C\u002Fh2>\u003Cblockquote>“Baz wanted to automate this missing layer of verification, bringing intent, behavior, and implementation into a single review workflow.”\u003C\u002Fblockquote>\u003Cp>What this actually means is: they stopped pretending a code review can answer product questions by itself. A diff can tell you what changed. It cannot reliably tell you whether the feature behaves like the design, matches the acceptance criteria, or preserves the interaction details a PM and designer care about.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780762746197-jynp.png\" alt=\"Baz’s review agent turns specs into merge checks\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>I’ve run into this same failure mode over and over. A component gets renamed, a prop gets wired, tests pass, and everyone assumes the feature is basically done. Then someone opens the preview and says, “Wait, the spacing is off,” or “That button state never appears,” or the classic, “This is technically correct, but it’s not what we asked for.” That gap between code and intent is where review systems get lazy.\u003C\u002Fp>\u003Cp>Baz’s answer is to treat the review as a product-validation problem, not just a code-validation problem. That’s a much harder target, but it’s also the one that matters. If you’re building an \u003Ca href=\"\u002Ftag\u002Fagent\">agent\u003C\u002Fa> to review code, you need it to inspect artifacts that describe the product, then observe the running app and compare the two.\u003C\u002Fp>\u003Cp>How to apply it: stop feeding your reviewer only the PR diff. Add the source of truth for behavior. In practice, that means pulling in design docs, tickets, acceptance criteria, screenshots, and live preview runs. If your review bot can’t see the intended result, it’s guessing. And guessing is how you get confident nonsense.\u003C\u002Fp>\u003Ch2>Figma and Jira are the real spec, not the comment thread\u003C\u002Fh2>\u003Cblockquote>“The Specification Subagent, powered by Amazon Bedrock, ingests both visual specifications from Figma and functional specifications from Jira.”\u003C\u002Fblockquote>\u003Cp>That line matters because it shows where Baz is getting its truth from. Not from a chat thread. Not from a half-remembered meeting. From the actual design and work-tracking systems people already use. That’s the first thing I’d copy if I were building something like this: don’t invent a new source of truth just to make the agent feel smart.\u003C\u002Fp>\u003Cp>What this actually means is the system has to translate two different kinds of specs. Figma carries visual intent: spacing, component hierarchy, colors, layout, state. Jira carries functional intent: what the user should be able to do, what counts as done, what edge cases matter. If you skip either one, the reviewer becomes lopsided. It either nags about pixels and ignores behavior, or it checks behavior and misses obvious UI regressions.\u003C\u002Fp>\u003Cp>I like this split because it mirrors how real teams work, even when they don’t admit it. Designers care about what users see. Product cares about what users can do. Engineers care about what the code does. A decent review agent has to reconcile all three, which is annoying, but that’s the job.\u003C\u002Fp>\u003Cp>Baz also uses MCP to query Figma and REST APIs for Jira, which is the part I’d pay attention to if I were implementing this myself. The point isn’t the protocol. The point is that the agent should pull structured requirement artifacts automatically when the PR lands. If the human has to hand-feed the bot every ticket, the workflow collapses fast.\u003C\u002Fp>\u003Cp>How to apply it: build a requirement ingestion step before any reasoning happens. Normalize the input into discrete checks like “button visible on mobile,” “empty state copy matches,” or “acceptance criterion 3 is satisfied.” Then keep the raw source links attached so reviewers can trace every judgment back to Figma or Jira. If you can’t point to the original artifact, the check is probably too fuzzy.\u003C\u002Fp>\u003Cul>\u003Cli>Visual spec inputs: Figma frames, component states, spacing, typography, hierarchy.\u003C\u002Fli>\u003Cli>Functional spec inputs: Jira acceptance criteria, user stories, edge cases, workflow rules.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Break one feature into many tiny verdicts\u003C\u002Fh2>\u003Cblockquote>“The system then spawns isolated sub-agent workers (one per requirement) tasked with the job of verifying the requirement.”\u003C\u002Fblockquote>\u003Cp>This is one of the few agent patterns I actually trust. One big agent trying to judge an entire feature is a mess. It drifts, it forgets details, and it starts acting like it has a coherent opinion when it really just has a vague impression. Baz splits the work into subagents, one per requirement, which is boring in the best possible way.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780762737508-wyz7.png\" alt=\"Baz’s review agent turns specs into merge checks\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>What this actually means is that each subagent gets a narrow job: verify one requirement against the implementation. That keeps the reasoning bounded. Instead of asking, “Did the feature work?” you ask, “Did this exact requirement happen in the live app?” That’s much easier to evaluate and much easier to debug when the agent gets it wrong.\u003C\u002Fp>\u003Cp>I’ve had to untangle systems where the agent output was a wall of text with no obvious fault line. You can’t tell whether it failed because the spec was unclear, the browser step was wrong, or the model simply hallucinated a conclusion. Subagents reduce that mess. If requirement 7 fails, I know where to look. That’s the difference between something I can operate and something I can only admire in a demo.\u003C\u002Fp>\u003Cp>Baz then rolls those findings up into a report generator. That matters too. Humans do not want 17 tiny agent transcripts dumped into Slack. They want a decision, a short explanation, and a link to the evidence. The subagents do the dirty work; the report generator turns it into something a team can act on.\u003C\u002Fp>\u003Cp>How to apply it: define a requirement schema first, then spawn one worker per requirement. Keep each worker’s output constrained to pass\u002Ffail plus evidence. If you need a summary, generate it after the checks are done. Don’t let the summary agent invent the facts. That’s how you end up with a polished lie.\u003C\u002Fp>\u003Cul>\u003Cli>Good unit of work: one acceptance criterion, one UI state, one interaction path.\u003C\u002Fli>\u003Cli>Bad unit of work: “review the whole feature” or “make sure this looks right.”\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>The browser is where the truth finally shows up\u003C\u002Fh2>\u003Cblockquote>“The Implementation Subagents are the core of this architecture… [they] render the actual implementation in a live Preview Environment and visually validate that the UI matches the intended Figma designs and that functionality behaves as specified in Jira.”\u003C\u002Fblockquote>\u003Cp>This is the part I care about most. Static analysis is useful, but it cannot tell you whether the app actually behaves correctly in the browser. Baz uses \u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fbedrock\u002Fagentcore\u002F\">Amazon Bedrock AgentCore\u003C\u002Fa> Browser Tool to open the preview environment, inspect the DOM, simulate events, and compare what it sees against the spec. That’s the right layer to validate product behavior because that’s where users live.\u003C\u002Fp>\u003Cp>What this actually means is the agent can click, type, inspect state, and observe visual output instead of making assumptions from source code alone. That’s a big deal. Plenty of bugs are invisible in code review because the code looks reasonable. The real failure only appears when the app is running, the state changes, and the UI doesn’t reflect what the spec promised.\u003C\u002Fp>\u003Cp>I’ve seen this with forms, modals, conditional rendering, and all the little interaction traps that make teams miserable. The code path is there. The component renders. But the state transition is wrong, or the animation hides a control, or a disabled state never appears. A browser-backed agent can catch that. A diff reviewer usually can’t.\u003C\u002Fp>\u003Cp>AgentCore also gives Baz the isolation and sandboxing they need so the browser sessions are safe and repeatable. That matters because once you let an agent click around in a preview environment, you want tight control. No one wants a review bot wandering off into random tabs, leaking context, or depending on somebody’s laptop state.\u003C\u002Fp>\u003Cp>How to apply it: wire your review agent to a real preview deployment and let it inspect the app like a user would. Use browser automation for stateful checks, visual checks, and interaction checks. If your reviewer only reads code, it’s blind to the thing users actually experience.\u003C\u002Fp>\u003Cp>If you want the tooling behind this approach, start with \u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fbedrock\u002F\">Amazon Bedrock\u003C\u002Fa> for model \u003Ca href=\"\u002Ftag\u002Finference\">inference\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fbedrock\u002Fagentcore\u002F\">AgentCore\u003C\u002Fa> for browser execution, and a preview environment that mirrors production behavior closely enough to matter. If the preview is fake, the review will be fake too.\u003C\u002Fp>\u003Ch2>Keep the model on interpretation, not infrastructure babysitting\u003C\u002Fh2>\u003Cblockquote>“Amazon Bedrock AgentCore became the foundation for building an AI code reviewer capable of validating real product behavior.”\u003C\u002Fblockquote>\u003Cp>Baz is doing something sensible here: they keep the model focused on reasoning, while AgentCore handles the browser execution and isolation. That separation is easy to miss, but it’s the difference between a system that scales and a system that turns into a maintenance hobby.\u003C\u002Fp>\u003Cp>What this actually means is the LLM should interpret specs, compare observations, and decide whether a requirement is met. It should not be spending its time worrying about browser lifecycle, sandbox setup, or how to keep sessions from stepping on each other. That stuff is infrastructure. Let the platform do platform work.\u003C\u002Fp>\u003Cp>I like this because it keeps the failure modes cleaner. If the model is wrong, I inspect the prompt or the requirement decomposition. If the browser session breaks, I inspect the execution layer. If everything is jammed together, every failure becomes a philosophical argument nobody wants to have.\u003C\u002Fp>\u003Cp>Baz also mentions observability, which is the other thing people skip until the first production incident. If you’re letting agents make judgments on live UI state, you need to know what they saw, what they clicked, and why they concluded pass or fail. Otherwise you’re just shipping automated confidence.\u003C\u002Fp>\u003Cp>How to apply it: separate reasoning from execution in your architecture. Keep the model’s job narrow and auditable. Log the browser steps, the DOM snapshots, the screenshots, and the final verdict. Then make sure a human can replay the path when something looks off.\u003C\u002Fp>\u003Ch2>Ship the verdict where developers already work\u003C\u002Fh2>\u003Cblockquote>“Once the review is complete, findings are distributed to the appropriate channels: comments are posted directly to the GitHub PR, notifications are sent to Slack for team visibility, and identified issues can be automatically linked back to Jira for tracking and resolution.”\u003C\u002Fblockquote>\u003Cp>This is the unglamorous part that determines whether the whole thing gets used. If the review lands in some separate dashboard, people will ignore it. Baz pushes the result back into \u003Ca href=\"\u002Ftag\u002Fgithub\">GitHub\u003C\u002Fa> PRs, Slack, and Jira, which is exactly where the team already lives. That’s not a nice-to-have. That’s the only way this stuff survives contact with reality.\u003C\u002Fp>\u003Cp>What this actually means is the agent’s output has to fit existing workflows. Developers want comments in the pull request. QA wants traceability. Product wants issues tied back to the ticket. If you force everyone to check a new system just to read the bot’s opinion, adoption dies quietly and nobody admits why.\u003C\u002Fp>\u003Cp>I’ve watched teams build excellent internal tools that nobody used because the output was trapped in the wrong place. Great logic, terrible delivery. Baz avoids that by routing findings to the channels that already carry review, coordination, and follow-up.\u003C\u002Fp>\u003Cp>How to apply it: decide where each kind of finding belongs before you build the agent. Put blocking issues in the PR. Put broader visibility in Slack. Put follow-up work in Jira. If the agent finds a problem and there’s no obvious destination for it, the workflow is incomplete.\u003C\u002Fp>\u003Cp>The other thing I’d copy here is the emphasis on automatic linking. A review comment without a trackable issue is just noise. If the agent can connect the finding back to a ticket, the team can fix it without re-litigating the context.\u003C\u002Fp>\u003Ch2>The template you can copy\u003C\u002Fh2>\u003Cpre>\u003Ccode># Spec Review Agent template for pull request validation\n\n## Goal\nValidate that a pull request matches product intent, design intent, and functional requirements before merge.\n\n## Inputs\n- GitHub pull request URL\n- Figma file or frame links\n- Jira issue links or acceptance criteria\n- Preview environment URL\n- Source repository access\n\n## Pipeline\n1. PR webhook triggers the review job.\n2. Fetch design artifacts from Figma.\n3. Fetch functional requirements from Jira.\n4. Normalize artifacts into discrete requirements.\n5. Spawn one subagent per requirement.\n6. For each requirement:\n   - inspect relevant source code\n   - open the live preview environment\n   - interact with the UI in a browser\n   - compare observed behavior to the requirement\n   - return pass\u002Ffail plus evidence\n7. Aggregate all subagent results.\n8. Post a summary to the GitHub PR.\n9. Send a notification to Slack.\n10. Create or update Jira issues for failures.\n\n## Requirement schema\n- id: string\n- source: figma | jira | both\n- type: visual | functional | interaction\n- description: string\n- expected_result: string\n- evidence_required: screenshot | dom_state | console_log | network_log | step_trace\n- severity: blocker | major | minor\n\n## Subagent output schema\n- requirement_id: string\n- verdict: pass | fail | needs_human_review\n- summary: string\n- evidence:\n  - screenshots\n  - DOM snapshots\n  - interaction steps\n  - relevant code references\n- confidence: low | medium | high\n- follow_up: string\n\n## Review prompt\nYou are a code review agent. Your job is to verify whether the implementation matches the requirement.\nUse the source code, the live preview environment, and the requirement artifact.\nDo not guess. If evidence is missing, mark needs_human_review.\nReturn only the schema fields requested.\n\n## PR comment format\n### Review summary\n- Pass: X\n- Fail: Y\n- Needs human review: Z\n\n### Blocking issues\n- [requirement_id] short description with evidence link\n\n### Non-blocking issues\n- [requirement_id] short description with evidence link\n\n### Notes\n- Link to screenshots\n- Link to browser trace\n- Link to Jira follow-up\n\n## Operating rules\n- Prefer concrete evidence over model intuition.\n- Keep each subagent focused on one requirement.\n- Separate execution logs from final reasoning.\n- Fail closed when the evidence is insufficient.\n- Always preserve traceability to the original spec.\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Ch2>Why this Baz pattern is worth stealing\u003C\u002Fh2>\u003Cp>My read is simple: Baz didn’t just add AI to code review. They changed what review is allowed to judge. That’s the real move. They took the review problem out of the narrow “does the diff look fine” box and rebuilt it around product evidence, browser behavior, and traceable requirements.\u003C\u002Fp>\u003Cp>If you’re building anything similar, don’t start with the model. Start with the question you actually want answered. Then figure out which artifacts prove it, how to break the work into bounded checks, and where the result should land so the team will use it. That’s the whole game.\u003C\u002Fp>\u003Cp>And yeah, the browser step is the part people will want to skip because it sounds fussy. I wouldn’t skip it. That’s where the app stops being theory and starts being the thing users touch.\u003C\u002Fp>\u003Cp>Source: \u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fmachine-learning\u002Fhow-baz-improved-its-ai-agent-code-review-accuracy-using-amazon-bedrock-agentcore\u002F\">https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fmachine-learning\u002Fhow-baz-improved-its-ai-agent-code-review-accuracy-using-amazon-bedrock-agentcore\u002F\u003C\u002Fa>. What I wrote here is my breakdown and template, not a copy of the original post.\u003C\u002Fp>","Baz’s Spec Review agent turns Jira and Figma into browser-backed pull request checks with Bedrock AgentCore.","aws.amazon.com","https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fmachine-learning\u002Fhow-baz-improved-its-ai-agent-code-review-accuracy-using-amazon-bedrock-agentcore\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780762746197-jynp.png","industry","en","16b6ff6e-a48b-4fac-8f8f-45ea58449a83",[17,18,19,20,21],"Amazon Bedrock AgentCore","AI code review","Figma","Jira","browser automation",[23,24,25],"Baz makes review check product intent, not just code diffs.","The strongest pattern is splitting one feature into requirement-level subagents.","Browser-backed validation catches behavior bugs static review misses.",0,"2026-06-06T16:18:31.315645+00:00","2026-06-06T16:18:31.296+00:00","d19fc184-5852-4c4d-9ec0-db0c4841ac17",{"tags":31,"relatedLang":42,"relatedPosts":46},[32,34,36,38,40],{"name":17,"slug":33},"amazon-bedrock-agentcore",{"name":21,"slug":35},"browser-automation",{"name":18,"slug":37},"ai-code-review",{"name":20,"slug":39},"jira",{"name":19,"slug":41},"figma",{"id":15,"slug":43,"title":44,"language":45},"baz-spec-review-agent-bedrock-agentcore-zh","Baz 怎麼把規格變成合併檢查","zh",[47,53,59,65,71,77],{"id":48,"slug":49,"title":50,"cover_image":51,"image_url":51,"created_at":52,"category":13},"2982171e-d440-41cf-b188-65cdda134338","vibe-coding-enterprise-software-change-management-en","Vibe coding is changing enterprise software work","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780774386263-f0ln.png","2026-06-06T19:32:33.912058+00:00",{"id":54,"slug":55,"title":56,"cover_image":57,"image_url":57,"created_at":58,"category":13},"bfc20a10-6bc0-42f6-ab76-83ac9846cfcb","5-things-to-know-about-metas-llama-3-rollout-en","5 things to know about Meta’s Llama 3 rollout","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780772571137-wtvq.png","2026-06-06T19:02:23.052358+00:00",{"id":60,"slug":61,"title":62,"cover_image":63,"image_url":63,"created_at":64,"category":13},"bcb80586-e23e-4822-b98e-d92b65870928","5-reasons-to-use-kimi-k2-5-on-cloudflare-en","5 reasons to use Kimi K2.5 on Cloudflare","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780769864414-tqp9.png","2026-06-06T18:17:17.730869+00:00",{"id":66,"slug":67,"title":68,"cover_image":69,"image_url":69,"created_at":70,"category":13},"138e82fa-a027-47b1-8259-2cb0f5811df0","5-diem-chinh-ve-thoi-tiet-ngay-mai-56-en","5 điểm chính về thời tiết ngày mai 5\u002F6","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780766277730-245y.png","2026-06-06T17:17:34.775812+00:00",{"id":72,"slug":73,"title":74,"cover_image":75,"image_url":75,"created_at":76,"category":13},"a032f5f2-5dbe-4356-9d17-ffaa0830d652","why-thanh-hoa-to-hue-weather-alerts-should-be-taken-seriousl-en","Why Thanh Hóa to Huế weather alerts should be taken seriously","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780765369933-2pox.png","2026-06-06T17:02:19.880819+00:00",{"id":78,"slug":79,"title":80,"cover_image":81,"image_url":81,"created_at":82,"category":13},"631dc392-127d-42b9-bd36-11c8c32a124d","4-market-signals-trumps-iran-talks-en","4 market signals in Trump’s Iran talks","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780759078488-7qig.png","2026-06-06T15:17:30.183196+00:00",[84,89,94,99,104,109,114,119,124,129],{"id":85,"slug":86,"title":87,"created_at":88},"d35a1bd9-e709-412e-a2df-392df1dc572a","ai-impact-2026-developments-market-en","AI's Impact in 2026: Key Developments and Market Shifts","2026-03-25T16:20:33.205823+00:00",{"id":90,"slug":91,"title":92,"created_at":93},"5ed27921-5fd6-492e-8c59-78393bf37710","trumps-ai-legislative-framework-en","Trump's AI Legislative Framework: What's Inside?","2026-03-25T16:22:20.005325+00:00",{"id":95,"slug":96,"title":97,"created_at":98},"e454a642-f03c-4794-b185-5f651aebbaca","nvidia-gtc-2026-key-highlights-innovations-en","NVIDIA GTC 2026: Key Highlights and Innovations","2026-03-25T16:22:47.882615+00:00",{"id":100,"slug":101,"title":102,"created_at":103},"0ebb5b16-774a-4922-945d-5f2ce1df5a6d","claude-usage-diversifies-learning-curves-en","Claude Usage Diversifies, Learning Curves Emerge","2026-03-25T16:25:50.770376+00:00",{"id":105,"slug":106,"title":107,"created_at":108},"69934e86-2fc5-4280-8223-7b917a48ace8","openclaw-ai-commoditization-concerns-en","OpenClaw's Rise Raises Concerns of AI Model Commoditization","2026-03-25T16:26:30.582047+00:00",{"id":110,"slug":111,"title":112,"created_at":113},"b4b2575b-2ac8-46b2-b90e-ab1d7c060797","google-gemini-ai-rollout-2026-en","Google's Gemini AI Rollout Extended to 2026","2026-03-25T16:28:14.808842+00:00",{"id":115,"slug":116,"title":117,"created_at":118},"6e18bc65-42ae-4ad0-b564-67d7f66b979e","meta-llama4-fabricated-results-scandal-en","Meta's Llama 4 Scandal: Fabricated AI Test Results Unveiled","2026-03-25T16:29:15.482836+00:00",{"id":120,"slug":121,"title":122,"created_at":123},"bf888e9d-08be-4f47-996c-7b24b5ab3500","accenture-mistral-ai-deployment-en","Accenture and Mistral AI Team Up for AI Deployment","2026-03-25T16:31:01.894655+00:00",{"id":125,"slug":126,"title":127,"created_at":128},"5382b536-fad2-49c6-ac85-9eb2bae49f35","mistral-ai-high-stakes-2026-en","Mistral AI: Facing High Stakes in 2026","2026-03-25T16:31:39.941974+00:00",{"id":130,"slug":131,"title":132,"created_at":133},"9da3d2d6-b669-4971-ba1d-17fdb3548ed5","cursors-meteoric-rise-pressures-en","Cursor's Meteoric Rise Faces Industry Pressures","2026-03-25T16:32:21.899217+00:00"]