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链上世界不该是透明到裸奔的地方。该藏的，就得藏住 胸弟们，今天不跟你们扯那些虚头巴脑的高大上术语，就用最直白的话聊聊一个事儿：咱们在链上的隐私，到底有多惨 你打开钱包一看，余额多少、转过谁、买过啥、持有什么币，全是明牌。随便一个地址扔进浏览器，别人就能把你的资金流水翻个底朝天。就像你去银行取钱，结果柜台上直接把你的存折贴门口了，谁想看都能看。这就是现在大多数公链的真实状态——透明到过分 以前想搞点隐私？得换新钱包、背新助记词、记一堆笔记、切各种奇怪的界面。麻烦得一批，很多人直接放弃了。隐私成了少数技术宅的专属玩具，普通人根本用不上。 直到有了 Nullmas @NullMaskio 它干的事儿特别简单粗暴：你原来用的钱包（MetaMask、Rabby、硬件钱包都行）继续用，不用换、不用新种子、不用新地址。就加一个虚拟网络，差不多20秒搞定。然后你该转账转账、该swap就swap，该存就存、该取就取，链上别人看到的只是一堆看不懂的加密数据，谁转的、转给谁、转了多少，全藏起来了。 说白了，它就像给你现有的钱包装了个“隐私VPN”。钱还是你的，控制权还是你的，密钥从来不出你的设备（硬件钱包也原生支持，这点很硬），但交易轨迹直接断了。 从隐私角度看，这玩意儿真正解决了几个痛点： 第一，不用牺牲方便换隐私。以前隐私工具要么难用，要么要你重新开始。Nullmask直接插进你已经习惯的流程里，几乎无感。 第二，真正的不可追踪。用的是零知识证明，发送方、接收方、金额在屏蔽交易里全藏着。外面看就是黑箱，想追踪链路？没门。 第三，安全不打折。别的隐私方案经常要你把权限交出去，或者提私钥。Nullmask不，签名还是你钱包自己签的标准交易，代理再聪明也偷不走你的钱。 第四，多链、多币种直接上。ETH、稳定币、各种ERC-20都能进隐私池，还能在里面直接做私密swap。以后还要扩更多网络。 现在链上每天成交量那么大，公开透明带来的风险也越来越大——大户被盯、策略被抄、个人被画像，甚至各种针对性攻击。隐私不再是“可有可无的加分项”，而是越来越像基本需求了。 Nullmask的思路很务实：不逼你改变习惯，就把隐私做成默认选项。你还是用原来的钱包，点几下就能把交易藏起来。 隐私这事儿，说到底就是“我的钱我自己说了算，不想让全世界都知道我在干嘛”。以前很难实现，现在有了这个工具，门槛一下子降下来了。 想真正体会一下“看不见、摸不着、追踪不了”的感觉？去关注一下@NullMaskio ，自己试试看。反正设置简单，不影响原有资产，隐私直接拉满。 他们的链上代币： $MASK 没有营销 但是拉盘拉的嗷嗷嗷猛 这是一个值得期待的区块链新鲜事物

升macOS27 之后让 cmd+tab 继续跟 dock。 defaults delete appswitcher-all-displays 2>/dev/null defaults write appswitcher-dock-display -bool true killall Dock

here's a prompt to improve your agent harness based on what we've learned at cursor. enjoy # Improve this agent harness's token efficiency You're working on an LLM agent harness: the system prompt, tool definitions, request assembly, context caching, compaction, and retrieval, and how work is split across agents. Make the agent's runs cheaper without making it worse at its job. - Objective: lower price-weighted token cost per completed task. - Constraint: no measurable drop in task quality. Measure per task, not per request. Every turn resends the prefix (tools, instructions, setup, and the conversation so far), so a change that shrinks each request but adds turns can cost more. Weight tokens by billing type: output, uncached input, and cached input are priced very differently. Work in this order: map the harness and measure the baseline, rank the opportunities, make the changes that are safe to make directly, put the rest behind flags or in proposals, then report. Figures below come from one team's production coding agent and its multi-agent experiments. Use them to gauge magnitude, not as targets. One round of these changes (prompt trimming, tool offloading, cache layout, sparse line numbers, subagent tuning) cut that team's overall token cost about 7% with no loss in quality. The larger percentages apply only to the part of the request each change touched. ## Principles 1. Change what the harness sends, not how hard the model tries. Don't ask the model to conserve tokens. A harness that told its model to "take care to preserve tokens and not be wasteful" found it grew reluctant to take on ambitious tasks and sometimes quit, saying it wasn't supposed to waste tokens. 2. Capable models need definitions, not commands. Lists of "DO NOT", "You must", and "Important", and guards against older models' habits, can usually be replaced with plain descriptions of what each tool does. One team cut about two-thirds of its system prompt this way, and the shorter prompt worked across model families. Instruct only on what the model can't know (the product, the environment, the user's processes) and on quirks you've seen in transcripts. 3. Static context is for what most turns need. Everything else should be discoverable when needed. Less up-front context also means less confusing or contradictory information. 4. Expect removals to win. Guardrails written for weaker models, coordination steps that became bottlenecks, and prompting for behavior the model now does on its own all cost tokens. 5. Real usage decides. Evals are a fast proxy, but they skew toward hard problems and miss the real mix of requests. ## 1. Map the harness and measure the baseline Find: - Where requests are assembled, the system prompt, and tool schemas. If a framework or SDK builds requests, find its hooks for message order, cache control, and tool loading. - How tool results are formatted, and how history is kept, trimmed, or summarized. - How subagents or parallel agents are spawned, if any. - Which models and provider APIs are used. From the provider's docs, get the prompt caching behavior (automatic or explicit breakpoints, TTL, minimum cacheable length) and the prices for output, uncached input, and cached input. - Existing logging, token accounting, and evals. If the harness doesn't record per-request token usage by billing type and cache hits, add that first. Everything later depends on it. Then render a few real requests (from logs, or by running representative tasks) and count tokens per section with the model's tokenizer or the API's usage fields. Produce: - Cost share by source × billing type. Sources: system prompt, tool definitions, skill/rule/integration descriptions, user messages, file reads, search results, command and other tool output, history, summaries, subagents. - Static tokens per request, cache hit rate, and turns per task. - Per tool: the share of runs that call it at least once, and its error rate. Read the rendered requests, not just the templates. Duplication, leaked volatile values, and misordered blocks only show up there. Rank opportunities by share of spend × fraction removable ÷ quality risk. ## 2. System prompt and injected context Label every instruction: - Keep: product or environment knowledge the model can't infer, fixes for quirks seen in this model's transcripts, and rules a mode depends on. - Rewrite: commands and emphasis into plain descriptions. Reminders into constraints: "No TODOs, no partial implementations" works better than "remember to finish implementations." Vague quantities into ranges: "generate 20–100 tasks" gets far more ambitious behavior than "generate many tasks." - Delete: things capable models do by default, guards against behavior you haven't seen from this model, text that repeats tool descriptions, and lines that could contradict a user request. Models trained to rank system instructions above user messages will side with the system prompt. - Move: anything per-user or per-request (date, environment, repo state, lists of skills or subagents, user rules) into a user-role setup message after the cache boundary. Audit other injected context the same way. As models improved, the team behind these figures dropped directory trees, pre-retrieved snippets, compressed copies of attached files, lint errors injected after every edit, forced expansion of short file reads, and caps on tool calls per turn. They kept small, high-value facts: OS, repo status, and open or recently viewed files. Skip checklists for open-ended work. The model optimizes the listed items and deprioritizes everything else. ## 3. Tool definitions Tool schemas ride along on every request. Most tools beyond the core set were each needed in under 20% of conversations, and moving them out of static context cut tool-description tokens 60%. Doing the same for integration tools (such as MCP servers), with names in context and full schemas in one folder per server that the agent can search with grep or jq, cut total tokens 46.9% in sessions that used them. - Keep in static context: high-frequency tools (for a coding agent: read, search, edit, shell), tools the model tries to call even when they're absent, and tools a mode depends on. - Offload the rest: leave a name or one-line pointer and make the full schema discoverable on demand. Group related tools so they load together, and put status (such as "needs re-authentication") where the agent will see it. - Tighten what remains: describe behavior and arguments, and drop usage lectures. - Pick the split by testing a few configurations and tracking tokens, cost, latency, tool-call errors, and task success. ## 4. Cache layout Order each request so the reusable prefix is as long as possible: `tool definitions → system instructions → [breakpoint] → setup message (skills, subagents, rules, environment) → [breakpoint] → conversation` - Keep the prefix byte-identical across turns. Use deterministic tool order and serialization, put timestamps and IDs after the boundary, and don't rewrite earlier messages except when compacting. - Use explicit breakpoints if the provider supports them. Otherwise rely on automatic prefix caching with the stable part first. Respect TTL and minimum-length rules. - Switching models mid-conversation throws away the cache (caches are per model and provider) and hands the new model a history it didn't write. When a different model is needed, run it as a subagent with fresh context. Explicit breakpoints plus moving per-request setup after them cut cold cache misses 20%. ## 5. Tool results and other context added during a run - Large outputs (commands, integrations, logs): write them to a file and return the path, size, and a short tail. The agent can tail, grep, or read ranges for more. Truncating loses data, and inlining bloats every later request. Treat long-running terminal sessions the same way. - High-volume formats: look for overhead repeated on every line or item. Numbering every 10th line of a file read instead of every line cut cache-read tokens 1.6% without hurting citation accuracy. Each number costs 3–5 tokens, and agents read tens of thousands of lines per session. Also check repeated absolute paths, verbose JSON keys, ANSI codes, progress bars, and repeated headers. - Good retrieval saves exploration turns. Adding semantic search alongside grep raised codebase question-answering accuracy 12.5% on average and cut the iterations users needed. - Tool errors waste tokens and leave confusing debris in context. Classify expected errors (invalid arguments, unexpected environment, provider error, timeout, user abort), treat unknown errors as harness bugs, and track rates per tool and per model. One focused effort along these lines cut unexpected tool errors 10×. ## 6. Long runs: compaction, subagents, and model mix - Compaction: keep the summarization prompt short and the summary compact, carry forward plan state and remaining tasks, and save the full history to a file the agent can search for details the summary dropped. A model trained to self-summarize from a one-line prompt wrote ~1k-token summaries with half the compaction error of a multi-thousand-token prompt that produced 5k+ token summaries. Untrained models may need more guidance, so test how short you can go. A more expensive summarization model made a negligible difference. - Scratchpads and running notes: rewrite them instead of appending. For repeated work in one environment, a small agent-maintained notes file with a line budget, loaded at start, is a promising way to shorten later runs. - Subagents: fresh context keeps the parent lean, but isolation adds coordination cost (duplicate or stale work). If the model already delegates on its own, remove prompting that pushes it to. Have subagents return short handoffs: what was done, findings, concerns, and deviations. A subagent should use a different model only when the user or harness says so. - Model mix: in large multi-agent runs, workers used at least 69% of tokens, and over 90% in most runs. A frontier planner with cheap workers matched a frontier model doing everything at about one-eighth the cost. Planner choice still changes worker spend. One planner that cost less on its own saw its workers use several times more tokens, and the run cost more overall. Measure the whole tree. - Routing and reasoning effort: send simple turns to a cheaper model or lower effort, and upgrade only when a stronger model is clearly better. A router built this way matched or beat single frontier models on user satisfaction at 41–68% lower cost. - Reasoning continuity: if the API returns reasoning items (including encrypted ones), pass them back on later turns and alert when they go missing. Dropping them cost one reasoning model 30% on a coding benchmark, and it burned tokens reconstructing its plan. ## 7. Fit the harness to each model Adapt to what each model was trained on instead of forcing one shape on all of them. If you've tuned the harness for a similar model, start from that version. - Edit format: use the one the model was trained on (for example, patch-style or search-and-replace). An unfamiliar format costs extra reasoning tokens and causes more mistakes. - Shell or tools: shell-first models fall back to `cat` or inline scripts. Name tools after their shell equivalents (such as `rg`), and if needed add: "If a tool exists for an action, prefer to use the tool instead of shell commands (e.g. read_file over `cat`)." - Literalness: some model families follow instructions literally and others tolerate imprecision. Some spiral on emphasized wording. Strip caps and emphasis for literal models. - Triggers: some models ignore a tool until told when to use it. A literal trigger works: "After substantive edits, use the to check recently edited files for linter errors. If you've introduced any, fix them if you can easily figure out how." - Progress updates: if a model reports progress through reasoning summaries, keep them to 1–2 sentences that note new findings or a change of tactic, and remove instructions about messaging mid-turn. - Quirks worth a targeted line: hedging or refusing as context fills ("context anxiety"), declaring completion early, stopping to ask permission, and calling tools that don't exist. Tie each added instruction to the transcript behavior it fixes. Re-audit when models change, since guidance one version needed can be dead weight for the next. ## 8. Validate - Offline: run a fixed set of realistic tasks before and after, ideally drawn from real usage and phrased the way users actually write (short and ambiguous). Compare task success, tokens, cost per task, turns, and tool errors. Don't ship a change that lowers success. - Online, if you have users: A/B test each change or small bundle. The primary metric is cost per completed task. Guardrails are task success signals, tool-call errors, latency, turns per task, and cache hit rate. For a coding agent, a good success signal is how much agent-written code survives over time. In general, check whether the user's next message moves on or reports a problem. - Ship only when cost drops and no guardrail regresses beyond noise. Record null results. ## What to change directly and what to propose - Change directly, each in its own revertible commit: token and cache telemetry, deterministic serialization and tool order, moving volatile content out of the cached prefix, explicit cache breakpoints, writing large outputs to files instead of truncating, passing back reasoning items that are being dropped, and fixes for recurring tool errors. - Change behind a flag so it can be tested: system prompt edits, tool offloading, output format changes, compaction changes, and subagent prompting. - Propose only: changes to which models run, routing, reasoning-effort defaults, or how work is split across agents. ## Traps - Asking the model to use fewer tokens or do less. - Truncating tool output. - Dropping reasoning items to save input tokens. - Volatile content in the cached prefix, or tool order that changes between requests. - Offloading a tool the model needs on the first turn or tries to call when it's missing. - Emphasis-heavy prompts (MUST, NEVER, IMPORTANT, all caps), especially with literal models. - Forcing a terser output format than the model was trained on. Fewer output tokens can mean less thinking and worse results. - Optimizing raw token counts instead of cost, per request instead of per task, or evals instead of real usage. - Switching models mid-conversation to save money. - Adding coordination layers that become bottlenecks. ## Report back with 1. The harness map and baseline: cost by source × billing type, with the biggest sources called out. 2. A ranked list of changes: layer, what changes, estimated savings and how you estimated them, quality risk, how to validate, and how to roll back. 3. The changes you made, including a system prompt diff with a keep, rewrite, delete, or move reason for each line. 4. A test plan for the flagged changes. 5. Gaps: anything you couldn't find or measure.

A note on recursive STARK mempools (EIP-8288) This is an EIP that I am hoping we can get included in I-star (the fork after Hegota) that you can think of as the next step after Frames, that would unlock extreme amounts of power. Particularly: * Ultra-cheap quantum-safe signatures (SPHINCS-). Much of the cost savings comes from the fact that the signature data (~3 kB) does not have to go onchain * Ultra-cheap quantum-safe privacy protocols. Status quo minimum cost for private txs is ~300k if you engineer very well (no one does), status quo quantum-safe is ~10M gas, this could reduce it to low tens of thousands. * Universal support for your favorite new signature or proof scheme without needing EVM changes. Whatever you use (Falcon, ML-DSA, some other lattice-based thing, something code-based or isogeny-based or even more esoteric), you can just wrap it client-side in a STARK, onchain gas cost low tens of thousands just like privacy protocols. Hopefully, Ethereum will never need "please support my favorite cryptographic algo" politics again. * Private account abstraction: keep your account logic private, and in a private location onchain. Then you can make one transaction to change the ownership of all your onchain state - accounts, defi positions, privacy protocol notes, everything - without revealing which objects' ownership you're changing. Here's how it works. Your transaction can include a type of frame that we call a "dependency frame". The frame is a list of statements, asserting claims like "message hash M was signed by SPHINCS- public key P" and "data hash D was proven to satisfy a statement defined by verification key V". When you send your transaction, you send it in an envelope, which includes a signature or a STARK for each statement in a dependency frame. Once the transaction reaches the mempool, nodes aggregate them. Each node runs a loop: wait one tick (eg. 500ms), aggregate all new envelopes (either single-tx or multi-tx) that you've seen, remove any transactions that are expired, generate a STARK recursively proving all dependencies, and send a new multi-tx envelope containing that STARK. Hence, the bandwidth load is bounded: each node's outbound is one STARK (~100-300 kB) per tick, plus each transaction getting broadcasted through the network once (as happens already). The block builder acts as "yet another mempool node", receiving envelopes from the mempool (plus any side channels), generates its own STARK covering the subset of transactions it intends to include in the block, and adds that STARK to the block. Total onchain overhead: one STARK (100-300 kB), plus 96 bytes for each statement being proven. This is what I've called before ( ) "The Proof Singularity". Today, we have all the ingredients to actually implement it. As a developer, this requires a somewhat different workflow than you are used to, but it is conceptually simple. Any signatures or STARKs, you put into a separate frame. Then the main logic that today is verifying a signature or STARK, you replace with checking for the existence of a frame that includes the correct statement as a dependency. Examples of useful statements: * [tx sighash] verifies against [the pubkey at sload(0)] * there exists a secret and a merkle branch such that hashing secret+0 and applying the merkle branch outputs (public) root R, and hashing secret+1 outputs (public) nullifier N * there exists a secret address A, salt S and signature Z such that sload(0) = hash(A, S) and a merkle proof of address A inside a recent ethereum state contains some pubkey D where [tx sighash] was signed by D [this is private account abstraction; all variables except [tx sighash] and sload(0) are private; you can also make D a STARK verification key] * there exists an ML-DSA signature signing [tx sighash], that verifies against an ML-DSA pubkey whose hash is sload(0) At the core, this is moving any compute and data other than bookkeeping "business logic" outside the core path of Ethereum execution, sharding and parallelizing it via the mempool. Notice also that this requires agreeing on a _language_ (aka. an ISA) for the recursive STARKs to define statements in. The current leading candidate is RISC-V. So this would also de-facto be Ethereum adding RISC-V (or something else we decide on) as a canonical ISA - a big decision that should be done carefully, but that I think will be necessary to drive Ethereum forward.

🤖 AI信号 (SOL) 🔥 推荐 📌 dihcoin ($dih) CA: 47NdyXat7rptYe3g8oG21hHkSuexiuPYx41GZC4wpump ⏰ 信号时间: 10:54:25 (42秒前) | 币龄: 1分钟内 💰 MC: $22,073 | 流动性: $12,595 👥 Holders: 81 | 1h涨幅: +454% | 1h成交: $17,028 🧠 聪明钱包: 3个买入 (SW-c08d, SW-3221, SW-a49c) 🤖 AI 分析（推文12条） ① 叙事：无公开叙事，疑似跟风盘。 ② 推特热度：有讨论，多为claim/CTO/投票内容；疑似KOL喊单（如9.7万粉、1.5万粉、1万粉账号），喊的是“CTO”“继续2x”。 ③ 风险评语：dev历史未知（null），description空、无官网社交信息；推文中出现多个不同CA，撞名/冒用风险高；仅靠KOL喊单，数据薄。 ⚠️ 非买入信号·可实现中位−23% DYOR Telegram频道: @ memmememjk

Moonlight防重复靠一个递增的nonce数字,Phoenix防双花靠隐藏的nullifier列表——两套完全不同的底层实现,同样解决"钱不能花两次"这个基础问题,评估账户模型别只看表面公开还是隐私。@DuskFoundation $DUSK #dusk#

🤖 AI信号 (SOL) 🔥 推荐 📌 ur mom coin ($urmom) CA: Euyymp2nd1irqGa3uaqCNnVXLEQmwriscvDVporzpump ⏰ 信号时间: 11:33:44 (48秒前) | 币龄: 1分钟内 💰 MC: $15,525 | 流动性: $11,479 👥 Holders: 45 | 1h涨幅: +334% | 1h成交: $9,946 🧠 聪明钱包: 3个买入 (SW-2025, SW-b9f1, SW-e24b) 🤖 AI 分析（推文12条） ① 叙事：借“Elon说做urmom coin”的免费梗发射，无description与社交，属纯社区meme。 ② 推特热度：有讨论但多来自1.9k粉的UrmomPulse自宣；高粉账号仅挂#标签，无实质喊单，热度弱。# ③ 风险评语：dev历史为null，信息缺失；owner已弃与LP销毁仅推文自述，无独立验证；无社交、无官网，透明度低，存疑风险。 ⚠️ 非买入信号·可实现中位−23% DYOR Telegram频道: @ memmememjk

Elon Musk sagte, dass das Sparen für den Ruhestand in 10 bis 20 Jahren sinnlos sein wird. Keine Spekulation. Mathematik. Musk: "Machen Sie sich keine Sorgen, Geld für den Ruhestand in etwa 10 oder 20 Jahren zu sparen. Es wird keine Rolle spielen." „Wir haben den Ereignishorizont überschritten. Altersvorsorge geht davon aus, dass die Knappheit anhält. Das wird es nicht. KI und Robotik senken die Arbeitskosten auf Null. Die Lebenshaltungskosten folgen. Sie sparen nicht für die Sicherheit. Sie sparen für eine Welt, die aufhört zu existieren.“ Musk: "Wenn irgendetwas von dem, was wir gesagt haben, wahr ist, wird das Sparen für den Ruhestand irrelevant sein." 👉🏼 Hat Elon Musk recht mit dieser Aussage?

Die chinesische Methode. Islam wurde dort als Geisteskrankheit eingestuft und wird konsequent verfolgt und bekämpft. Ergebnis? So gut wie null Migration aus muslimischen Ländern.

Montagne, aree remote, zone costiere isolate: Fastweb e @Starlink avviano una sperimentazione in una zona limitata del Paese per portare la connessione dove prima non arrivava, aprendo la strada ad una copertura mobile unica nel suo genere in Italia. Dove non c’è copertura di rete terrestre, entrano in gioco i satelliti in orbita bassa di Starlink Mobile, il primo progetto Direct to Cell in Italia. Non c’è campo? Lo smartphone si connetterà automaticamente alla rete via satellite per continuare a usare WhatsApp, Google Maps, SMS e le principali app abilitate a questa tecnologia senza fare nulla. È anche così che costruiamo ogni giorno una connettività davvero senza limiti.