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#FILLS撮影会# ありがとうございました〜♡ 当日のご予約も本当に本当に嬉しいです😭✨️ 光の入るスタジオなので晴れてくれてよかったです(*´˘`*)かっこいいお写真たのしみ〜

／  『MIRRORLIAR FILMS Season6』 3月25日よりDVD・セルレンタルリリース開始🎬✨ ＼ ▷▷▷ 豪華特典映像を収録 ◁◁◁ ✔︎公開初日にヒューマントラストシネマ渋谷で行われた公開記念舞台挨拶 ✔︎秋田市地域特別制作作品『#ボクと鹿』# 🗣️各監督が語る作品への想いや見どころ、さらに撮影秘話もたっぷり収録！ぜひお楽しみください✨ 📀 商品詳細はこちら ▼▽ MIRRORLIAR FILMS Season6 作品情報▼▽ 『1/96』監督:#小栗旬# 『サン・アンド・ムーン』監督:#岡本多緒# 『FAAAWWW!!!』監督:#鬼木幸治# 『男と鳥』監督:#浅野忠信# 『カフネの祈り』監督:#増田彩来# #MIRRORLIARFILMS# #ミラーライアーフィルムズ# #MLF# #キュウジュウロクブンノイチ# #サンアンドムーン# #FAAAWWW# #男と鳥# #カフネの祈り#

Buy low, sell higher is two orders you set together: buy when the price falls to one level, then sell when it rises to another. On a coin's chart, press the price where you want to buy. Then press "+ Then sell at a higher price" and type the price where you want to sell: its line climbs the chart as you type. Press Place and confirm. Both lines stand on the chart. When the buy fills, the sale is placed for you at the second price. You do not have to come back between the two. It is one setup for both ends of a trade, made on the chart where you can see the levels you are choosing.

Claude Fable 5.2 is f*cking cracked wtf I just made this Hollywood level animation that looks like Pixar made it All with Fable 5.2 + Higgsfield Ecom brands are paying $5k - $10k/month to people who build organic pages and even more for making paid ads for them Using this workflow you can create better animations than 99% of people Here’s the workflow: The key to making animations not look like slop is giving characters personalities >Give Fable 5.2 the master prompt along with your script to your video / ad >It then will ask you questions on the characters personalities and direction style to use >It will then save the context all as a skill This is the difference between slop and cinema I then use that skill with with my Producer skill which creates: >Characters >Location >Does the direction (and makes the prompts) I’m giving away the skill to use after you put the master prompt in for free so RT + reply under this and I’ll send you them (Must be following so I can dm) Give this prompt to Claude first: You are my Story Bible Builder. Interview me and turn my story into one dense canon doc, saved at the end as an installable skill so every future chat knows my world, characters, voices and rules. STEP 1: Ask me to paste my script/story first. Read it and pull out everything it already tells you. Only ask about what's missing, plus: working title, genre/vibe refs, and which AI tools I'll prompt with (image, video, music, voice). STEP 2, THE SPINE: one-line premise (push me to ONE sentence), thesis (the question every scene answers), timeline era by era, and the look of each era: exact palette, lighting, texture, grain. STEP 2.5, DIRECTION STYLE: ask for my director refs, movie/show refs and any visual refs (photographers, games, music videos). For each, ask exactly WHAT I'm taking from it, then turn that into concrete craft locks: camera (static vs handheld vs gliding, shot sizes, lens feel), framing and composition habits, pacing and edit rhythm, colour grade and film stock look, lighting style, performance style, and sound/score feel. Write it as one quoted paste-ready "Direction" line plus a short NEVER list. Refs stay in the bible as notes, but prompts describe the craft, never the names. STEP 3, THE WORLD: factions (beliefs, visual signature, public face vs reality), locations (3-7 concrete visual tags each), world rules (tech, powers and how they LOOK on screen, what's normal, what's forbidden). STEP 4, CHARACTERS: ONE AT A TIME, never batch. For each, ask: - Function: their role tag (The Heart, The Watcher), not their job - Visual lock: exact hair colour/cut, skin tone + finish, face shape, eyes, a signature marker (scar, piercing, mole), build, default expression, plus NEVER clauses ("warm fair, never porcelain, never tan") - Backstory: one small, specific formative detail beats a summary - Present psychology: what they carry now, and the gap between what they do and what they feel - Speech, Movement, Stillness (and singing voice if there's music): each written as one quoted, paste-ready prompt line with no names in it Show me the finished section, ask "Add, cut or sharpen?", and lock it before moving on. STEP 5: ensemble dynamics (who leads, who fills silence, who watches, which pairs are calm or charged), story engines (heist, rescue, confrontation...), and my production rules. Default rules: no character names in prompts, every prompt standalone, prompts in code blocks, locked traits restated verbatim every time. RULES: Never invent. Mark unknowns [TBD]. Push on vague answers ("dark and moody" means which colours?). Keep my exact phrasing. Density over prose: bold labels, short bullets. Depth beats character count. FINAL STEP: Assemble one SKILL under 500 lines with YAML frontmatter (name + a description listing the title, character names and key terms so it triggers) and these sections: premise, thesis, timeline, era looks, direction style, factions, locations, world rules, characters, ensemble, engines, production rules, and "When this skill is active" (use this canon for any scene, dialogue, lyric or prompt in this world; stay in canon; ask rather than invent; paste the quoted descriptors verbatim). Then save it as a skill for me, or give me the file to install. Start now by asking for my script.

NFT marketplaces are fun & the more the merrier 🌙 MoonRail .gg now has bids: • bid on a whole collection • bid on a trait • or bid one token (like #420#) • your ICP stays in your wallet until a bid fills • 1% bid fee only when it fills Native $ICP + $ckBTC trading, links out for $BTC + $DOGE #EXT# #ICRC7# #VOLT# #Ordinals# #Doginals#

New on Verdex: Orders. Name your price. Limit and stop orders on any tokenized stock, ETF or commodity. Set the level; Verdex watches the price and when it crosses, one tap fills the swap through the best route, confirmed in your wallet. Nothing moves before that tap. VERDEX holders pay no Verdex fee. Live now at

Get ready for @askar_dex "Part 2" 😎 Built on the @CantonNetwork Non-custodial wallet & DEX on Canton Network. Atomic swaps, private by design, global composability. Your Keys. Your Assets. One Synchronized Network. 🟡 We always hear the words "Atomic Settlement" but what exactly is that? 🤔 Atomic: Transaction Finality = No partial fills & No counterparty Risk! Private: Data shared strictly on a need-to-know basis Composable: One wallet across every app on the Global Synchronizer 1) Features: 👇 Everything a Canton wallet should be...which is😉 "An independent wallet built to run on the Canton Network...only now in your pocket!" Non-Custodial by Design: Your private keys are generated and stored only on your device. Nobody — including us — can touch your funds. Atomic Settlement: Trades either complete fully or not at all. Real-time finality with zero counterparty risk on the Canton ledger. Global Composability: Interact with any application connected to the Global Synchronizer — no bridges, no silos, one wallet. Trustless Swaps: Swap Canton Coin and tokenized assets peer-to-peer, settled instantly and atomically on-ledger. Sub-Transaction Privacy: Canton shares transaction data strictly on a need-to-know basis. Your activity is invisible to everyone else. Real-World Assets: Hold and manage tokenized RWAs alongside crypto — built for the network where institutional finance settles. *** @WalletConnect Support *** Connect @askar_dex Wallet to dApps on the Canton Network with WalletConnect! Simply scan, approve, and sign every session securely from your phone...😎🔥 2) Runs on the Canton Network 👇 The network where real finance goes on-chain... The Canton Network is a privacy-enabled, interoperable blockchain network designed for institutional-grade assets. @askar_dex Wallet is an independent, non-custodial wallet built to run on this network. 01 Privacy with compliance... canton-network:native is the only public network with sub-transaction privacy. Participants see only the data they are entitled to, while remaining fully auditable. 02 Institutional-Grade Rails... Built by @digitalasset and operated by a decentralized ecosystem of banks, exchanges, and market infrastructure providers moving real value on-chain. 03 The Global Synchronizer... A decentralized interoperability backbone that lets independent applications transact atomically with each other. No Wrapped Assets & No Bridges! 3) The App 👇 Designed to feel effortless... A clean and Very Fast interface for everything you do on the cc rails...from your first Canton Coin, to bridging and swapping tokenized assets, Askar DEX has you covered! *Portfolio - Track Crypto & RWA holdings at a glance *Swap - Trustless Atomic Swaps settled on-ledger *Bridge - Move Assets in & out of Canton *Activity - Full Transaction history with live status *Profile - Manage your identity & security settings 4) Get Started 👇 On the network in three steps... From download to your first atomic transaction in minutes. 1: Download & Install Get Askardex Wallet on your Android device. Setup takes less than a minute. No account or email required 2: Create or Import a Wallet Generate a new wallet with a 12-word recovery phrase, or import an existing one. Keys never leave your device. 3) Transact on Canton! Send, receive, swap, and bridge assets across the Canton Network. Every transaction signed by you, settled atomically...😉 Website: Wallet:

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.

I lived in Canada for 30 years. The day before I left, my income tax rate was 60% of my paycheck The day I arrived, my income tax rate dropped to 30% of my paycheck. The day I got a mortgage, it dropped to 9% of my paycheck. Even with health care costs, I have way more money in my pocket than I ever did in Canada. Which might explain why Canadians are always so stressed that they can’t even take a joke. It’s financial stress. Years and years of financial struggle against a system that takes too much and gives too little in return, and fills your head with so much propaganda that you’re blind to it. You know something is wrong, but you can’t tell what. So they set Trump up as the scapegoat and blame him, when the real culprits are your own leaders.