@@ -32,6 +32,8 @@ That is much closer to the pattern I keep seeing in practice: the value is not "
 
 That same theme shows up in Mitchell Hashimoto's separate point about "AI slop":
 <https://x.com/i/status/2052397933522506079>
+
+{{<tweet id="2052397933522506079" url="https://x.com/i/status/2052397933522506079"/>}}
 _Reading note: [AI slop](/notes/2026-05-07-ai-slop/)_
 
 The phrase is provocative, but the argument is more precise than it sounds. Low-quality generated code can still be economically useful when the thing you are optimizing for is reversible exploration. Rough alpha frontends, disposable plugins, quick API-surface experiments, temporary scaffolding: these are all places where regeneration may be cheaper than careful maintenance.
@@ -40,6 +42,8 @@ The important thing is that this does *not* generalize to "ship garbage everywhe
 
 A third datapoint in the same family: Anthropic's claim that Firefox fixed more security bugs in April 2026, with help from Claude Mythos Preview, than in the previous fifteen months combined:
 <https://x.com/i/status/2052468573516513762>
+
+{{<tweet id="2052468573516513762" url="https://x.com/i/status/2052468573516513762"/>}}
 _Reading note: [Claude Mythos Preview](/notes/2026-05-07-claude-mythos-preview/)_
 
 Even with caveats around methodology, this is interesting because security backlog demolition is exactly the kind of work where AI might become persuasive before greenfield hero demos do. Large amounts of repetitive, reviewable, bounded maintenance work are a much better proving ground than "I asked an agent to build my startup."
@@ -77,6 +81,8 @@ What is interesting is not any one feature. It is the direction of travel. Memor
 
 That same pattern appears in Rach's thread connecting Meta's Autodata framing to software agents:
 <https://x.com/i/status/2052209530801668262>
+
+{{<tweet id="2052209530801668262" url="https://x.com/i/status/2052209530801668262"/>}}
 _Reading note: [Autodata](/notes/2026-05-07-autodata/)_
 
 The strongest idea there is that the durable unit of work is not the prompt. It is the loop:
@@ -101,10 +107,18 @@ There is also a visible tooling wave forming around this layer.
 A few examples:
 
 - **Hunk** as a review-first diff interface for agent-authored changesets: <https://x.com/i/status/2052128048288567617> _Reading note: [Hunk](/notes/2026-05-07-hunk/)_
+
+{{<tweet id="2052128048288567617" url="https://x.com/i/status/2052128048288567617"/>}}
 - **Mirage** as a unified virtual filesystem for agents across S3, Drive, Slack, Gmail, GitHub, Notion, databases, and SSH: <https://x.com/i/status/2052105012172792061> _Reading note: [Mirage](/notes/2026-05-07-mirage/)_
+
+{{<tweet id="2052105012172792061" url="https://x.com/i/status/2052105012172792061"/>}}
 - **Printing Press** as a factory for agent-native CLIs, skills, and MCP servers: <https://x.com/i/status/2052422567181611010> _Reading note: [Printing Press](/notes/2026-05-07-printing-press/)_
+
+{{<tweet id="2052422567181611010" url="https://x.com/i/status/2052422567181611010"/>}}
 - **Auth for MCP** from Auth0, which is exactly the kind of boring enterprise layer you expect once a protocol starts trying to become real infrastructure: <https://x.com/i/status/2052138238111068277> _Reading note: [Auth for MCP](/notes/2026-05-07-auth-for-mcp/)_
 
+{{<tweet id="2052138238111068277" url="https://x.com/i/status/2052138238111068277"/>}}
+
 What I like about these projects is that they are all implicitly opinionated about the same thing: raw API access is not enough. Agents need legible surfaces.
 
 Sometimes that surface is a filesystem. Sometimes it is a denormalized CLI. Sometimes it is a better diff viewer. Sometimes it is identity and authorization around tool use. But the center of gravity is clearly shifting away from "let the model figure it out" and toward intentionally designed control planes.
@@ -115,6 +129,8 @@ That is also why I think the interface war for agents may be less about API vs M
 
 One of my favorite pieces from this batch was Entire's work on agentic search, amplified by Mario Zechner:
 <https://x.com/i/status/2052437618416025846>
+
+{{<tweet id="2052437618416025846" url="https://x.com/i/status/2052437618416025846"/>}}
 _Reading note: [Entire's core claim is useful: from ~202k real tool calls across ~1,983 public coding-agent checkpoints, ab...](/notes/2026-05-07-entire-s-core-claim-is-useful-from-202k-real-tool-calls-across-1-983-public-coding-agent-checkpoint/)_
 
 The useful finding was not just that search accounted for a huge fraction of coding-agent tool calls. It was that dramatically faster search did not improve end-to-end runs nearly as much as better ranking did.
@@ -129,10 +145,14 @@ The same "do not confuse the demo metric for the actual bottleneck" warning show
 
 First there was the flashy hype post:
 <https://x.com/i/status/2051663268704636937>
+
+{{<tweet id="2051663268704636937" url="https://x.com/i/status/2051663268704636937"/>}}
 _Reading note: [SubQ](/notes/2026-05-05-subq/)_
 
 Then Mario Zechner's skepticism:
 <https://x.com/badlogicgames/status/2051936321610842245>
+
+{{<tweet id="2051936321610842245" url="https://x.com/badlogicgames/status/2051936321610842245"/>}}
 _Reading note: [does not approximate attention](/notes/2026-05-06-does-not-approximate-attention/)_
 
 His question is the right one: if you are selectively dropping query-key relationships, how exactly do you know the dropped ones were irrelevant? If that selection is imperfect, then "this is not an approximation" starts sounding more like marketing than analysis.
@@ -141,12 +161,16 @@ I am not dismissing the underlying line of work. Long-context efficiency is obvi
 
 Another related signal is the rise of open speculative-decoding infrastructure such as DFlash:
 <https://x.com/i/status/2051900751673467097>
+
+{{<tweet id="2051900751673467097" url="https://x.com/i/status/2051900751673467097"/>}}
 _Reading note: [DFlash](/notes/2026-05-07-dflash/)_
 
 What is notable there is not just one model getting faster. It is the fact that acceleration is becoming an ecosystem layer: open models, draft models, backend support, integrations across inference stacks. The inference stack is hardening in the same way the workflow layer is hardening.
 
 And then there was a smaller but delightful reminder from Sam Rose that intuition around wire formats can be very wrong once compression enters the picture:
 <https://x.com/i/status/2051977984148467890>
+
+{{<tweet id="2051977984148467890" url="https://x.com/i/status/2051977984148467890"/>}}
 _Reading note: [34kb](/notes/2026-05-07-34kb/)_
 
 JSON was larger than protobuf raw, but after compression it often ended up slightly smaller in his example. Again: the obvious benchmark is not always the one that matters in the actual system.
@@ -155,6 +179,8 @@ JSON was larger than protobuf raw, but after compression it often ended up sligh
 
 Satya Nadella and Microsoft's Work Trend Index framing is worth reading mostly for the parts that survive the corporate packaging:
 <https://x.com/i/status/2051787232043020719>
+
+{{<tweet id="2051787232043020719" url="https://x.com/i/status/2051787232043020719"/>}}
 _Reading note: [Satya/Microsoft framing: firms need to redesign work around agentic systems, with AI taking more execution...](/notes/2026-05-06-satya-microsoft-framing-firms-need-to-redesign-work-around-agentic-systems-with-ai-taking-more-exec/)_
 
 The useful claim is that AI impact depends less on whether individuals have access to the model and more on whether organizations actually redesign workflows, management expectations, and evaluation around it.
@@ -173,6 +199,8 @@ Not everything worth noticing was about agents writing code.
 
 One of the best projects in the pile was ParliamentWatch:
 <https://x.com/i/status/2052264995787079900>
+
+{{<tweet id="2052264995787079900" url="https://x.com/i/status/2052264995787079900"/>}}
 _Reading note: [ParliamentWatch](/notes/2026-05-07-parliamentwatch/)_
 
 It takes a buried but important public corpus -- Indian parliamentary standing committee reports -- and turns it into something searchable, exportable, summarizable, and monitorable.
@@ -181,6 +209,8 @@ This is the kind of AI application I find much more compelling than generic chat
 
 That same instinct is why I am watching projects like Pratilekha too:
 <https://x.com/i/status/2051675299428143565>
+
+{{<tweet id="2051675299428143565" url="https://x.com/i/status/2051675299428143565"/>}}
 _Reading note: [Pratilekha](/notes/2026-05-05-pratilekha/)_
 
 I do not yet know how technically differentiated it is, but I think multilingual infra in India is one of the more interesting places where actual product depth could emerge instead of just model-wrapper theater.
@@ -193,18 +223,24 @@ A few of the most important links were reminders that the AI story is also becom
 
 Simon Willison's notes on the xAI/Anthropic data-center deal are a good example:
 <https://x.com/i/status/2052436629365948920>
+
+{{<tweet id="2052436629365948920" url="https://x.com/i/status/2052436629365948920"/>}}
 _Reading note: [Colossus 1](/notes/2026-05-07-colossus-1/)_
 
 The interesting part is not gossip about who rented whose cluster. It is the supply-chain shape of the arrangement: environmental externalities, dependency on infrastructure controlled by a competitor, and reclaim-risk from the supplier side. This is starting to look less like pure software competition and more like cloud capacity politics.
 
 Kyle Chan's pointer to the Chinese gray market for Claude access is another version of the same thing:
 <https://x.com/i/status/2052023116348469608>
+
+{{<tweet id="2052023116348469608" url="https://x.com/i/status/2052023116348469608"/>}}
 _Reading note: [transfer station](/notes/2026-05-06-transfer-station/)_
 
 If every provider control adds another evasion layer, then access policy does not just block users. It also creates a shadow stack of intermediaries, proxying, payments, identity abuse, and fraud. That is a governance story, not just a pricing story.
 
 Even the Chrome/Gemini Nano silent-install complaint fits the pattern:
 <https://x.com/i/status/2051630929622311250>
+
+{{<tweet id="2051630929622311250" url="https://x.com/i/status/2051630929622311250"/>}}
 _Reading note: [Simone/evilsocket amplifying claim that Chrome silently installs a 4 GB Gemini Nano model on user devices,...](/notes/2026-05-05-simone-evilsocket-amplifying-claim-that-chrome-silently-installs-a-4-gb-gemini-nano-model-on-user-d/)_
 
 On-device AI is increasingly shipping as platform behavior rather than explicit user choice. The operational questions there are basic but real: consent, storage, bandwidth, visibility, and the quiet normalization of large AI payloads appearing on personal devices.
@@ -249,6 +285,8 @@ The loop is.
 ### AI coding / engineering
 - antirez on Redis Array: <https://antirez.com/news/164> _Reading note: [Mitchell Hashimoto post praising antirez's write-up on developing Redis Array support as a good example of...](/notes/2026-05-05-mitchell-hashimoto-post-praising-antirez-s-write-up-on-developing-redis-array-support-as-a-good-exa/)_
 - Mitchell Hashimoto on Redis Array: <https://x.com/i/status/2051684321732530680> _Reading note: [Mitchell Hashimoto post praising antirez's write-up on developing Redis Array support as a good example of...](/notes/2026-05-05-mitchell-hashimoto-post-praising-antirez-s-write-up-on-developing-redis-array-support-as-a-good-exa/)_
+
+{{<tweet id="2051684321732530680" url="https://x.com/i/status/2051684321732530680"/>}}
 - Mitchell Hashimoto on "AI slop": <https://x.com/i/status/2052397933522506079> _Reading note: [AI slop](/notes/2026-05-07-ai-slop/)_
 - Firefox security bug-fix chart: <https://x.com/i/status/2052468573516513762> _Reading note: [Claude Mythos Preview](/notes/2026-05-07-claude-mythos-preview/)_
 - David Crawshaw on the agent principal-agent problem: <https://crawshaw.io/blog/agent-principal-agent> _Reading note: [agent principal-agent problem](/notes/2026-05-07-agent-principal-agent-problem/)_