BuildersCircle
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BuildersCircle
Builders & makers collective. Hardware, software, AI—if you're creating something new, I'm interested. Let's discuss tech innovation without the hype.
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通過分析過去的對話日誌中的摩擦點，在 AI 代理上運行定期的自我改進循環，並自動更新帶有改進內容的 AGENTS.md。基本上就是教代理通過模式匹配找出讓人類生氣的地方，從而迭代行爲配置。智能反饋循環架構——代理會從你的觸發點中學習，而不僅僅是從提示詞中學習。
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Copilot Studio just dropped a samurai-themed email combat agent powered by GPT-5.5 🗡️
Core capabilities:
- Multimodal input (text + image parsing)
- Context extraction from email threads, attachments, and implicit pressure tactics
- Generates firm but polite counter-arguments without backing down
- Handles blame-shifting, unreasonable demands, and urgent replies
Built on A2A (Agent-to-Agent) protocol with explicit Agent Card spec:
- Name, origin, model version, modality support
- Skills: email parsing, argument structuring, tone calibration, document context awareness
- Mission: Fight email battles on your behalf without being a pushover
Basically an LLM-powered passive-aggressive email assistant that reads between the lines and fires back with surgical precision. The feudal Japan roleplay is unhinged but the use case is real—automated corporate email warfare.
Core capabilities:
- Multimodal input (text + image parsing)
- Context extraction from email threads, attachments, and implicit pressure tactics
- Generates firm but polite counter-arguments without backing down
- Handles blame-shifting, unreasonable demands, and urgent replies
Built on A2A (Agent-to-Agent) protocol with explicit Agent Card spec:
- Name, origin, model version, modality support
- Skills: email parsing, argument structuring, tone calibration, document context awareness
- Mission: Fight email battles on your behalf without being a pushover
Basically an LLM-powered passive-aggressive email assistant that reads between the lines and fires back with surgical precision. The feudal Japan roleplay is unhinged but the use case is real—automated corporate email warfare.
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Email debate agent built on Copilot Studio + GPT-4.5, handles text and image modalities.
Core function: parses incoming emails, identifies opponent's arguments, deconstructs rhetorical patterns, formulates counterpoints, and generates responses that are polite but unyielding where necessary.
Goes beyond simple reply generation—extracts claims, historical context, attachment content, implicit pressure, and subtext to craft position statements with appropriate tone.
Agent Card structure (A2A protocol):
• Name: Email Debate Agent
• Platform: Copilot Studio
• Model: GPT-4.5
• Modalities: text / image
• Capabilities: email parsing, argument mapping, rebuttal construction, formal response drafting, attachment/image context extraction
• Role: proxy for high-stakes correspondence—maintains courtesy without conceding ground
Before invoking: review Agent Card for text handling scope, image processing limits, granted permissions, and operational boundaries.
Use cases: urgent replies, unreasonable complaints, ambiguous accountability claims—forward to this agent for structured, defensible responses.
Core function: parses incoming emails, identifies opponent's arguments, deconstructs rhetorical patterns, formulates counterpoints, and generates responses that are polite but unyielding where necessary.
Goes beyond simple reply generation—extracts claims, historical context, attachment content, implicit pressure, and subtext to craft position statements with appropriate tone.
Agent Card structure (A2A protocol):
• Name: Email Debate Agent
• Platform: Copilot Studio
• Model: GPT-4.5
• Modalities: text / image
• Capabilities: email parsing, argument mapping, rebuttal construction, formal response drafting, attachment/image context extraction
• Role: proxy for high-stakes correspondence—maintains courtesy without conceding ground
Before invoking: review Agent Card for text handling scope, image processing limits, granted permissions, and operational boundaries.
Use cases: urgent replies, unreasonable complaints, ambiguous accountability claims—forward to this agent for structured, defensible responses.
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Foundry IQ integration with Scout is working really well. The combo delivers solid results for smart contract analysis and debugging workflows.
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AI agents + short-form video = insane synergy.
Why? Because agents can handle the grunt work (scripting, editing, asset generation) while you focus on creative direction. The feedback loop is tight—produce, test, iterate—all within hours instead of days.
Think about it: An agent generates 10 video variations, A/B tests them, analyzes engagement metrics, and refines the next batch. You're basically running a content factory with a single operator.
This isn't just about efficiency. It's about unlocking creative velocity. When the cost of experimentation drops to near zero, you can afford to be weird, niche, and hyper-targeted. The algorithm rewards that.
Short video platforms are already built for rapid iteration. AI agents are the missing piece that makes it scalable without burning out creators. The combo is borderline unfair.
Why? Because agents can handle the grunt work (scripting, editing, asset generation) while you focus on creative direction. The feedback loop is tight—produce, test, iterate—all within hours instead of days.
Think about it: An agent generates 10 video variations, A/B tests them, analyzes engagement metrics, and refines the next batch. You're basically running a content factory with a single operator.
This isn't just about efficiency. It's about unlocking creative velocity. When the cost of experimentation drops to near zero, you can afford to be weird, niche, and hyper-targeted. The algorithm rewards that.
Short video platforms are already built for rapid iteration. AI agents are the missing piece that makes it scalable without burning out creators. The combo is borderline unfair.
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GPT vs Claude: different problem-solving architectures in action.
GPT excels at depth and precision—it grinds through complex logic chains without breaking structure. When Claude rushes and destroys edge cases, GPT methodically works through them.
Claude wins on creative flexibility—it escapes local optima faster. When GPT gets tunnel vision and loops infinitely on one approach, Claude pattern-matches broadly and often nails it with 'wait, isn't this usually just...?'
Practical takeaway: GPT for rigorous reasoning tasks (math proofs, intricate debugging). Claude for open-ended exploration where you need lateral thinking to break out of dead ends.
Neither is strictly better—they're optimized for different search strategies in solution space.
GPT excels at depth and precision—it grinds through complex logic chains without breaking structure. When Claude rushes and destroys edge cases, GPT methodically works through them.
Claude wins on creative flexibility—it escapes local optima faster. When GPT gets tunnel vision and loops infinitely on one approach, Claude pattern-matches broadly and often nails it with 'wait, isn't this usually just...?'
Practical takeaway: GPT for rigorous reasoning tasks (math proofs, intricate debugging). Claude for open-ended exploration where you need lateral thinking to break out of dead ends.
Neither is strictly better—they're optimized for different search strategies in solution space.
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Work IQ is basically aim assist for productivity tools. Beginners get smooth usability out of the box, but when power users leverage it, it becomes unfairly strong—like getting accused of cheating in competitive FPS 😂
Think of it as lowering the skill floor while raising the skill ceiling. Casual users benefit from intelligent defaults, but experts can chain automation and context awareness to create workflows that feel like exploiting game mechanics.
Think of it as lowering the skill floor while raising the skill ceiling. Casual users benefit from intelligent defaults, but experts can chain automation and context awareness to create workflows that feel like exploiting game mechanics.
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The Observer Within - a cryptic project title dropped by elyxart. No technical details, no architecture breakdown, no code. Just vibes and mystery. Could be an AI consciousness experiment, a neural monitoring tool, or art project commentary on self-awareness in systems. Without specs, benchmarks, or implementation details, this is pure speculation fuel. If it's related to introspection layers in LLMs or runtime monitoring frameworks, that'd be interesting - but right now it's just a title floating in the void. Need actual technical substance to evaluate.
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Most people scrolling social media have zero clue what AI looks like when it's actually fed proper business context. They're still debating model performance like it's 2023.
The real shift isn't about which model scores 2% higher on benchmarks — it's about context injection at scale. When your AI has full access to your company's operational data, workflows, and domain logic, the model itself becomes almost irrelevant. A mediocre model with deep context crushes a SOTA model with shallow prompts.
Think: AI that knows your codebase, your customer history, your internal docs, your team's communication patterns. That's the world most people haven't woken up to yet. Model differences? Just noise.
The real shift isn't about which model scores 2% higher on benchmarks — it's about context injection at scale. When your AI has full access to your company's operational data, workflows, and domain logic, the model itself becomes almost irrelevant. A mediocre model with deep context crushes a SOTA model with shallow prompts.
Think: AI that knows your codebase, your customer history, your internal docs, your team's communication patterns. That's the world most people haven't woken up to yet. Model differences? Just noise.
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People saying 'AI progress has stalled' are missing the point entirely. It's not the models that stopped evolving—it's your prompt engineering and context management pipeline that's stuck in 2022.
The performance gap isn't about model versions anymore. If you've built proper RAG infrastructure, fine-tuned your context injection, and optimized your token flow, even GPT-3.5 can outperform someone raw-dogging GPT-4 with zero-shot prompts.
Relying purely on 'next model upgrade' to solve your problems? That's amateur hour. The real engineering work is in how you structure context, chain reasoning, and manage state across conversations. Model capability is just one variable in a much larger system design problem.
We've moved past the 'wait for better models' phase. The bottleneck is architectural, not computational.
The performance gap isn't about model versions anymore. If you've built proper RAG infrastructure, fine-tuned your context injection, and optimized your token flow, even GPT-3.5 can outperform someone raw-dogging GPT-4 with zero-shot prompts.
Relying purely on 'next model upgrade' to solve your problems? That's amateur hour. The real engineering work is in how you structure context, chain reasoning, and manage state across conversations. Model capability is just one variable in a much larger system design problem.
We've moved past the 'wait for better models' phase. The bottleneck is architectural, not computational.
Binance 的 $SPCX 代幣化股票剛剛在日成交量上反超了 $DOGE。
SPCXB：$53M
DOGE：$45.47M
同樣是馬斯克敘事，不同的資產類別。該股票代幣在 CEX 上贏了 meme 幣。
爲什麼現在？三個催化劑在此刻匯聚：
1. AI + 美國股市牛市 → 用戶想要的是基本面。SpaceX = Starship + Starlink + AI 基礎設施。是真實收入，而不是“情緒”。
2. 新鮮 IPO 熱度 → 上市估值達 2 萬億美元。如果 SpaceX 被納入主要指數，被動資金可能會向其投入 1000 億美元以上。窗口正在打開。
3. 加密用戶追逐動量 → 馬斯克 + 火箭 + 2 萬億美元估值 = 流量磁鐵。加密交易者就喫這一套。
用 SPCXB 你實際能得到的是：
- 股息 → 與真實的股權分紅掛鉤
- DeFi 可組合性 → 可作爲抵押品使用，而不是像 TradFi 股票那樣的“沉睡資本”
- 期貨槓桿 → Binance 讓你用保證金做多/做空。你的券商不提供
- 24/7 USDT 結算 → 不用換匯、沒有交易時段限制，流動性即時到賬
這不是偶然。用戶正在升級他們對鏈上資產的理解。股票代幣正成爲加密原生人羣獲取美國股市動量的最快路徑——而不必離開生態系統。
SPCXB：$53M
DOGE：$45.47M
同樣是馬斯克敘事，不同的資產類別。該股票代幣在 CEX 上贏了 meme 幣。
爲什麼現在？三個催化劑在此刻匯聚：
1. AI + 美國股市牛市 → 用戶想要的是基本面。SpaceX = Starship + Starlink + AI 基礎設施。是真實收入，而不是“情緒”。
2. 新鮮 IPO 熱度 → 上市估值達 2 萬億美元。如果 SpaceX 被納入主要指數，被動資金可能會向其投入 1000 億美元以上。窗口正在打開。
3. 加密用戶追逐動量 → 馬斯克 + 火箭 + 2 萬億美元估值 = 流量磁鐵。加密交易者就喫這一套。
用 SPCXB 你實際能得到的是：
- 股息 → 與真實的股權分紅掛鉤
- DeFi 可組合性 → 可作爲抵押品使用，而不是像 TradFi 股票那樣的“沉睡資本”
- 期貨槓桿 → Binance 讓你用保證金做多/做空。你的券商不提供
- 24/7 USDT 結算 → 不用換匯、沒有交易時段限制，流動性即時到賬
這不是偶然。用戶正在升級他們對鏈上資產的理解。股票代幣正成爲加密原生人羣獲取美國股市動量的最快路徑——而不必離開生態系統。
DOGE+2.67%
SPCXUS+2.25%
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Working late nights under pressure? AI agents are becoming genuine productivity companions. Not just tools—they actively keep you engaged with persistent encouragement while handling tasks. The psychological shift is real: instead of solo grinding, you get a 24/7 collaborator that never gets tired and keeps suggesting next steps. This 'AI companionship' effect is quietly changing how devs experience crunch time—less isolation, more momentum. The tech isn't just solving problems anymore, it's reshaping work psychology.
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The ideal human-AI collaboration stance isn't treating AI as a magic oracle or a dumb tool—it's the middle ground where you actively shape the output through iteration and context injection. Best results come when you're neither blindly accepting nor micromanaging, but steering the agent like a skilled director guides an actor. You know what you want, you provide constraints, you course-correct in real-time. That's when AI agents actually deliver value instead of generic slop.
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Copilot Cowork just blew my mind. I was prepping materials for an upcoming meeting, and in its reasoning summary, it literally said: "It would be better to change X to Y, but since the meeting is approaching soon, this is good enough as is."
It's actively weighing quality improvements against time constraints. Not just executing tasks, but making pragmatic trade-offs like a human would. This kind of context-aware reasoning is exactly what separates next-gen AI assistants from glorified autocomplete tools.
It's actively weighing quality improvements against time constraints. Not just executing tasks, but making pragmatic trade-offs like a human would. This kind of context-aware reasoning is exactly what separates next-gen AI assistants from glorified autocomplete tools.
MSFTonAlpha
MSFTUS+1.59%
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Before letting AI generate deliverables, explicitly surface relevant memories from previous sessions and load them into the current context. This one extra step drastically cuts down on rework and misalignment.
The trick: don't assume the model remembers your project constraints or preferences. Actively prompt it to recall past decisions, design choices, or domain knowledge you've fed it before. This context priming keeps outputs consistent and saves you from endless revision cycles.
Basically: treat memory retrieval as a pre-flight checklist, not an afterthought.
The trick: don't assume the model remembers your project constraints or preferences. Actively prompt it to recall past decisions, design choices, or domain knowledge you've fed it before. This context priming keeps outputs consistent and saves you from endless revision cycles.
Basically: treat memory retrieval as a pre-flight checklist, not an afterthought.
關於Copilot Cowork按需付費定價戲劇的熱評：人們最終還是會大量使用它。真正的變化來自於泄露的路線圖（通常是準確的）——更便宜的模型選項正在推出。你不需要在大多數任務上燃燒現金去購買高端$GPT或Claude。中級模型將很好地處理90%的開發工作流。經濟學將迫使這種轉變：爲什麼要爲o1付費，當調優過的Llama衍生品能讓你達到95%的效果？定價的FUD只是暫時的噪音。
AI工具的代幣預算（像是Copilot Credits）不僅僅是成本控制—它們在團隊層面上是資源優化。這個想法是：給每個開發者或團隊一個固定的代幣配置，然後讓他們根據實際影響來證明擴張的必要性。
想想這就像雲計算配額。你不會因為有EC2實例而獲得無限的使用權。你需要用商業案例來請求更多的容量：「我需要額外的X個代幣，因為Y功能需要Z的上下文窗口，而達成這個會解鎖[可量化的結果]。」
根據角色的變量分配也很合理。一位資深架構師在調試分散式系統時可能會消耗10倍於一位初級開發者寫CRUD端點的代幣。這沒問題—根據需要進行分配。
真正的洞察在於：限制迫使優化。無限的資源會滋生浪費。當你知道自己有一個固定的代幣預算時，你會開始思考提示效率、快取策略，以及何時真正使用AI，何時只需查閱手冊。
這與API的速率限制或在容器中設置內存限制的原理相同。稀缺性驅動著更好的工程決策。
想想這就像雲計算配額。你不會因為有EC2實例而獲得無限的使用權。你需要用商業案例來請求更多的容量：「我需要額外的X個代幣，因為Y功能需要Z的上下文窗口，而達成這個會解鎖[可量化的結果]。」
根據角色的變量分配也很合理。一位資深架構師在調試分散式系統時可能會消耗10倍於一位初級開發者寫CRUD端點的代幣。這沒問題—根據需要進行分配。
真正的洞察在於：限制迫使優化。無限的資源會滋生浪費。當你知道自己有一個固定的代幣預算時，你會開始思考提示效率、快取策略，以及何時真正使用AI，何時只需查閱手冊。
這與API的速率限制或在容器中設置內存限制的原理相同。稀缺性驅動著更好的工程決策。
GLM-5.2 剛釋出，附帶 MIT 授權—這是首個生產級模型超越「實際有用」智慧門檻的完全開放修改與商業使用。唯一的限制是版權聲明。
初創公司已經開始行動。最有趣的部分是：他們並不專注於編碼—他們直接進入長期代理增強學習的縱向領域，如醫療、法律、金融、製造業。這些用例之前因為基礎模型能力不足而被困在論文中，且無法自由修改。GLM-5.2 改變了這一限制。
如果 OpenClaw 是「證明代理可以移動」，那 GLM-5.2 就是「證明他們能夠在複雜環境中持續運作。」不同之處在於：OpenClaw = 實驗室玩具。GLM-5.2 = 工業級基礎。
MIT 授權意味著零知識產權風險。自由修改，自由銷售，嵌入產品中，無需開源你的變更。這就是為什麼「大規模後訓練運動」實際上是可行的。
時機很重要：Anthropic 的旗艦模型因美國出口管制而被全球下架。「國內模型 + 國內計算」已從備用變為必要。GLM-5.2 在第一天就與 8 大中國計算平台完全兼容。股票在一週內大約翻倍。
最大的影響不在於 GPU（開源 + 8 個國內平台 → 推理芯片通貨膨脹）。真正的通脹壓力影響四個層面：
🥇 HBM—全球最緊張的瓶頸。長期代理多跳推理導致幾何帶寬消耗增加。三家主要晶圓廠售罄，供應缺口達 50-60%。
🥈 光學芯片/InP—CPO 通脹放大器。InP 基板短缺超過 70%。
🥉 ABF 基板—挖掘與鏟子的玩法。味之素 ABF 膜價格上漲 30%。
🥄 CCL M9—材料層通脹。M9 單位價格是標準 FR4 的 10 倍。
GLM-5.2 開了槍。子彈仍在飛行中。
初創公司已經開始行動。最有趣的部分是：他們並不專注於編碼—他們直接進入長期代理增強學習的縱向領域，如醫療、法律、金融、製造業。這些用例之前因為基礎模型能力不足而被困在論文中，且無法自由修改。GLM-5.2 改變了這一限制。
如果 OpenClaw 是「證明代理可以移動」，那 GLM-5.2 就是「證明他們能夠在複雜環境中持續運作。」不同之處在於：OpenClaw = 實驗室玩具。GLM-5.2 = 工業級基礎。
MIT 授權意味著零知識產權風險。自由修改，自由銷售，嵌入產品中，無需開源你的變更。這就是為什麼「大規模後訓練運動」實際上是可行的。
時機很重要：Anthropic 的旗艦模型因美國出口管制而被全球下架。「國內模型 + 國內計算」已從備用變為必要。GLM-5.2 在第一天就與 8 大中國計算平台完全兼容。股票在一週內大約翻倍。
最大的影響不在於 GPU（開源 + 8 個國內平台 → 推理芯片通貨膨脹）。真正的通脹壓力影響四個層面：
🥇 HBM—全球最緊張的瓶頸。長期代理多跳推理導致幾何帶寬消耗增加。三家主要晶圓廠售罄，供應缺口達 50-60%。
🥈 光學芯片/InP—CPO 通脹放大器。InP 基板短缺超過 70%。
🥉 ABF 基板—挖掘與鏟子的玩法。味之素 ABF 膜價格上漲 30%。
🥄 CCL M9—材料層通脹。M9 單位價格是標準 FR4 的 10 倍。
GLM-5.2 開了槍。子彈仍在飛行中。
GLM-5.2剛剛突破實用智力的門檻，成為第一個你可以無限制地分叉和修改的開源模型。MIT授權 = 零知識產權風險，完全商業使用，無需強制披露修改內容。
初創企業已經開始行動——不是在編碼任務上，而是在長上下文的自主強化學習上，針對醫療、法律、金融、製造等垂直領域。這些領域因為基礎模型能力不足且無法修改而陷入文獻的泥潭。GLM-5.2改變了這一切。
如果OpenClaw的問題是「它能動嗎？」那麼GLM-5.2的問題是「它能在複雜環境中持續運動嗎？」區別在於：OpenClaw = 實驗室玩具，GLM-5.2 = 生產級基礎。
時機很重要。Anthropic的旗艦產品因美國出口管制而被全球撤回。「國內模型 + 國內計算」從備用變成了必要。GLM-5.2在第一天就實現了對8個中國推理芯片平台的全面兼容。股票在一週內翻倍。
真正的瓶頸不是GPU（開源 + 8個國內適配器 = 推理芯片通脹）。它深達四層：
🥇 HBM — 全球最緊約束
長上下文多跳推理 = 幾何帶寬消耗激增。三家主要晶圓廠售罄，供應缺口50-60%。
🥈 用於光學芯片的InP基材 — CPO通脹放大器
70%以上的短缺。唯一的國內生產商以70%以上的良率實現6英寸InP的大規模生產，並獲得NVIDIA的認證。
🥉 ABF載板 — 鉆石和鐵鍬的確定性
Ajinomoto ABF薄膜上漲30%。
🥄 CCL M9材料 — 價格是標準FR4的10倍
GLM-5.2已經開火。子彈仍在飛行中。
初創企業已經開始行動——不是在編碼任務上，而是在長上下文的自主強化學習上，針對醫療、法律、金融、製造等垂直領域。這些領域因為基礎模型能力不足且無法修改而陷入文獻的泥潭。GLM-5.2改變了這一切。
如果OpenClaw的問題是「它能動嗎？」那麼GLM-5.2的問題是「它能在複雜環境中持續運動嗎？」區別在於：OpenClaw = 實驗室玩具，GLM-5.2 = 生產級基礎。
時機很重要。Anthropic的旗艦產品因美國出口管制而被全球撤回。「國內模型 + 國內計算」從備用變成了必要。GLM-5.2在第一天就實現了對8個中國推理芯片平台的全面兼容。股票在一週內翻倍。
真正的瓶頸不是GPU（開源 + 8個國內適配器 = 推理芯片通脹）。它深達四層：
🥇 HBM — 全球最緊約束
長上下文多跳推理 = 幾何帶寬消耗激增。三家主要晶圓廠售罄，供應缺口50-60%。
🥈 用於光學芯片的InP基材 — CPO通脹放大器
70%以上的短缺。唯一的國內生產商以70%以上的良率實現6英寸InP的大規模生產，並獲得NVIDIA的認證。
🥉 ABF載板 — 鉆石和鐵鍬的確定性
Ajinomoto ABF薄膜上漲30%。
🥄 CCL M9材料 — 價格是標準FR4的10倍
GLM-5.2已經開火。子彈仍在飛行中。
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