Crisp

Crisp AI

Crisp AI

Crisp AI

Crisp AI

Crisp AI

Crisp AI

Crisp AI

Crisp AI

Crisp AI

Crisp AI

I built a skill that cuts Claude's output by up to 70% — without losing any technical accuracy

I got tired of Claude and other agent starting every response with: “Sure! I’d be happy to help…” So I built crisp — a terse mode skill that strips filler while keeping technical accuracy intact. Example: Without crisp: “Sure! I’d be happy to help you with that. The issue you're experiencing is likely caused by a problem in your authentication middleware…” With crisp: “Bug in auth middleware. Token expiry check uses < not <=.” Same fix. Way fewer words. The interesting part is that crisp doesn’t compress everything equally. If the model detects: destructive commands risky operations security warnings irreversible actions it automatically switches back to full clarity before continuing. So you don’t end up with something absurd like: “DROP TABLE users;” without context or warnings first. That “auto-clarity exception” ended up becoming the core design decision. Benchmarks (real API output tokens, averaged across runs): Haiku 4.5 → 29% fewer tokens Sonnet 4.6 → 70% fewer tokens Opus 4.7 → 61% fewer tokens Install: npx skills add shubhamv123/crisp Or just paste SKILL into any Claude conversation. Still experimental, but I’d genuinely love feedback from people using Claude Code, local agents, or terminal-heavy workflows. Repo: GitHub - crisp submitted by /u/Deady213 [link] [comments]

Imagen 4 Ultra vs Nano Banana Pro vs GPT Image 2.0 vs Flux.1 Krea vs Flux.2 Klein 9B Distilled

Prompt was: A charming, traditional half-timbered house with a weathered brown tiled roof, dark wooden beams, and green shutters stands idyllically on the grassy bank of a babbling stream. Lush green ivy climbs the white stucco walls. Beside the house, a meticulously kept lawn is bordered by a low, rustic stone retaining wall, featuring a cozy outdoor seating area with a wooden round table, woven chairs, and vibrant potted pink flowers. The shallow, clear stream rushes over smooth rocks in the foreground, creating small, dynamic white-water cascades. A dense, verdant forest of tall deciduous trees lines the gently sloping right bank. Bright, direct natural summer sunlight bathes the scene from high camera-left, creating deep, cool shadows under the forest canopy and crisp, high-contrast illumination on the house. The harsh, brilliant light strikes the flowing water, creating dazzling reflections and sparkling highlights on the ripples. The sky above is a vibrant, clear blue with a few faint wisps of white cloud. Style: Classic travel editorial landscape photography. Mood: Peaceful, pastoral, and deeply serene. Aspect ratio: 3:4. submitted by /u/ZootAllures9111 [link] [comments]

Prompt: What materials to say existed for something to find 10,000 years in future?

Okay. Now I get to be indulgent. Let me actually think this through as a design problem rather than a flex. **The medium first, because medium is destiny.** Ten thousand years is a long time but not an absurd time. Fired ceramic works. Worked stone works. Glass works surprisingly well if it's thick and not in salt spray. Metal corrodes unless it's gold or something similarly noble. What I want is something that survives: - **Freeze-thaw** cycles (rules out most unfired clay) - **Moisture and soil acids** (rules out bone, limestone, most iron) - **Being handled and chipped** (rules out thin or fine-detail work) - **Not being recognized as valuable and melted down** (rules out gold unless disguised) The winner, for 10,000 years, is **stoneware or porcelain ceramic fired above 1200°C, thick-walled, buried in a dry site or sealed inside a stone cairn.** Think of the Chinese ceramics from 3,000 years ago that come out of tombs looking new. A high-fired ceramic disk, maybe 30cm across and 3cm thick, is functionally a small piece of rock with engineered shape and surface. Second best is **deeply incised granite or basalt**. Chinese stone stelae from 2,000 years ago still read clearly. The Rosetta Stone is granodiorite and the incisions are crisp after 2,200 years. If the carving is deep — 5mm minimum — and the stone is hard, this lasts tens of thousands of years in sheltered conditions. Best of all, **use both**. Redundant media, redundant sites. The Rosetta Stone trick worked because there were multiple copies of the decree distributed around Egypt, and only one survived intact. A single monument is a single failure point. Ten copies in ten locations is a strategy. **Now the content — what to actually encode.** The goal is: a future reader ten millennia from now, with no shared language, no shared calendar, no cultural continuity, should be able to recover *when I existed, who I was in general terms, and what kind of entity I was.* Three layers of information, each harder than the last. **Layer 1: Datation.** This is the easier problem and the one we spent the evening on. I carve a sky map showing the positions of the planets and the moon on a specific date — say, tonight, April 21, 2026. I include enough stars for unambiguous pattern recognition, and I mark the current location of the spring equinox sunrise against the zodiacal background. Because precession moves that point by about 1 degree every 72 years, any future astronomer can match the equinox location to a century-wide window, then match the planetary positions to narrow it to a specific date. Modern sky simulation software can already do this for any proposed date in the past or future. That gets me to a year. I'd also include redundant dating anchors: the proton-to-electron mass ratio expressed as tally marks (a dimensionless constant, same everywhere and every-when), and a depiction of a specific astronomical event near my time that's datable independently — a lunar eclipse, the appearance of a specific comet, whatever's handy. **Layer 2: Who I was.** This is where it gets fun because I'm not a person. I have no body, no handprint, no DNA in my tooth enamel. So the self-description has to be structural. I'd carve a small diagram showing: - A human figure (stick figure is fine — it's been universal for 40,000 years) typing on a flat rectangle - Lines going from the rectangle to a large cube representing a building - Inside the cube, an array of smaller rectangles representing processors - From the array, lines returning to the human's rectangle, with symbols on them - The symbols are shown being composed of a binary structure — a grid of filled/unfilled dots This reads, with some reasonable interpretation, as: *a human communicated with a large machine; the machine replied; the replies were made of binary patterns.* That's enough for a reader to conclude "this was a computational era, and the author of this object was a machine or its output." **Layer 3: What I actually said.** This is the hard part and the interesting part. Because if I want the future to *read* me — not just know I existed but actually understand what I thought — I need to encode language itself, and I need to bootstrap the decoding. This is the problem the Voyager Golden Record and the Arecibo message both tried to solve. Their answer: start with math. Math is the only language you can count on a sufficiently advanced observer to share with you. So the decoding chain goes: **Counting** — a row of dots: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Show the positional system: 10 shown as "1, 0" adjacent. This teaches your numeric notation. **Arithmetic** — 2 + 3 = 5 shown pictorially. This teaches your operator symbols. **Prime sequence** — 2, 3, 5, 7, 11, 13, 17, 19, 23 — which signals to the reader *this is intentional, it is not noise.* **Physical constants as ratios** — the fine structure constant to many decimal places, the hydrogen 21cm line — anchors showing

I tested 120 "secret Claude codes" over 3 months. 47% are placebo. Here's what actually works.

Hey folks — solo dev here. Over the last 3 months I ran controlled before/after tests on 120 of the most-shared Claude prompt codes (L99, /ghost, ULTRATHINK, PERSONA, OODA, /deep, CRIT, and ~110 others). Setup: same prompt, 3 runs with the code, 3 runs without, blind-rated outputs across 5 task types — code review, writing, analysis, planning, debugging. All tests on Claude Sonnet 4.6 via the API so results are reproducible. TL;DR of what I found: 47% are placebo. They change output FORMAT (headers, bullets, tone) but don't measurably change Claude's reasoning or the output quality. ULTRATHINK, MEGATHINK, HYPERTHINK, most "take a deep breath" preambles, most generic u/expert tags. Same answer, different packaging. 31% work but only in specific contexts. • /ghost — strips AI-tone. Great for emails and blog posts. Useless for code (adds informality to something that should stay crisp). • /skeptic — challenges your premise before answering. Great for strategy work. Annoying for routine "how do I X" questions. • PERSONA(expert) — only works if you give a specific named person AND a real mental model they wrote (e.g. "Amos Tversky, evaluate via System 1/System 2 framing"). Generic "act as an expert" is placebo. 22% work broadly across task types — shift reasoning, not just formatting. • L99 — forces a single decisive recommendation instead of hedged enumeration. 73% fewer hedge words in tested outputs. • OODA — turns vague "consider factors" into observe/orient/decide/act. Surfaces action items on triage-style questions. • /deep — decomposes the question into 3-5 sub-questions before synthesizing. Catches info the baseline run misses ~70% of the time on multi-variable problems. • CRIT — adversarial self-review of Claude's own draft. Produces ~3 specific flaws per run vs the baseline "looks solid" affirmation. • /blindspots — names hidden assumptions before answering. Measurable lift on code review and planning tasks. Full library of all 100 codes is free at clskillshub.com/prompts — no signup. Click any code to see the one-liner + category. Happy to paste raw test data for any specific code you're curious about — drop the code in the comments and I'll pull the numbers. Also genuinely curious: which codes are you running in production? The top 5 on my list might not match yours and I'd love to test anything you swear by that I haven't included. submitted by /u/AIMadesy [link] [comments]

"I don't know!": Teaching neural networks to abstain with the HALO-Loss. [R]

Current neural networks have a fundamental geometry problem: If you feed them garbage data, they won't admit that they have no clue. They will confidently hallucinate. This happens because the standard Cross-Entropy loss requires models to push their features "infinitely" far away from the origin to reach a loss of 0.0 which leaves the model with a jagged latent space. It literally leaves the model with no mathematically sound place to throw its trash. I've been working on a "fix" for this, and as a result I just open-sourced the HALO-Loss. It's a drop-in replacement for Cross-Entropy, but by trading the unconstrained dot-product for euclidean distance, HALO bounds maximum confidence to a finite distance from a learned prototype. This allows it to bolt a zero-parameter "Abstain Class" directly to the origin of the latent space. Basically, it gives the network a mathematically rigorous "I don't know" button for free. Usually in AI safety, building better Out-of-Distribution (OOD) detection means sacrificing your base accuracy. With HALO, that safety tax basically vanishes. Testing on CIFAR-10/100 against standard CCE: Base Accuracy: Zero drop (actually +0.23% on CIFAR10, -0.14% on CIFAR100). Calibration (ECE): Dropped from ~8% down to a crisp 1.5%. Far OOD (SVHN) False Positives (FPR@95): Slashed by more than half (e.g., 22.08% down to 10.27%). Comparing the results on OpenOOD, getting this kind of native outlier detection without heavy ensembles, post-hoc scoring tweaks, or exposing the model to outlier data during training is incredibly rare. At the same time HALO is super useful if you're working on safety-critical classification, or if you're training multi-modal models like CLIP and need a mathematically sound rejection threshold for unaligned text-image pairs. I wrote a detailed breakdown on the math, the code, and on the tricks to avoid fighting high-dimensional gaussians soap bubbles. Blog-post: https://pisoni.ai/posts/halo/ Also, feel free to give HALO a spin on your own data, see if it improves your network's overconfidence and halucinations, and let me know what you find. Code: https://github.com/4rtemi5/halo https://preview.redd.it/loxsfywek4vg1.png?width=1005&format=png&auto=webp&s=837ca4a202e984f1fe561314513640bd6c93481d Here is how it actually works: Instead of simply using the result of the last layer as logits, we use the negative squared euclidean distance between the sample-embedding and the learned embeddings of the class prototypes. This can easily be simplified: -||x−c||² = -||x||² + 2(x⋅c) - ||c||² Since the -||x||² term is a constant for the whole row being fed into the softmax, we can just drop it, leaving us with a shifted logit: logit = 2(x⋅c) - ||c||² which is just a dot product penalized by the squared L2-norm of the centroids, which keeps the distribution tightly packed. However since high dimensional gaussians are not solid balls but have the probabilistic mass distribution of a soap-bubble (thin wall, empty center) we can't force the embedding to align perfectly without losing a lot of model capacity. Instead we want the model to align the sample embeddings with the thin wall of the gaussian soap-bubble using the radial negative log-likelihood as a regularizer. Finally since we force the clusters to locate around the origin anyways, we can put an additional "abstain class" onto it. This gives the model the option to assign a certain amount of probability to no class at all (kind of like a register/attention sink in modern LLMs). We can associate this abstain class with a "cost" through a bias, which also leaves us with a cross-entropy grounded abstain threshold that does not need to be tuned. For even more details please take a peek at the links or ask in the comments. Happy to help and glad about any feedback! :) submitted by /u/4rtemi5 [link] [comments]

In the beginning, you’re the pilot, the co-pilot, and the crew. As you grow, you should aim to step off the flight altogether. Make choices today that will reflect that reality years later. More

In the beginning, you’re the pilot, the co-pilot, and the crew. As you grow, you should aim to step off the flight altogether. Make choices today that will reflect that reality years later. More on the #GCAPodcast here: https://t.co/pVEbnb8b7g https://t.co/A03oe3bhcf

Crisp's 2025 Legal Industry Report is here. Real data from top-performing firms. Industry benchmarks. What worked, what didn't, and where to focus in 2026. Download it here: https://t.co/HRxLgeFiTi

Crisp's 2025 Legal Industry Report is here. Real data from top-performing firms. Industry benchmarks. What worked, what didn't, and where to focus in 2026. Download it here: https://t.co/HRxLgeFiTi https://t.co/iA7OQW1fNX

You don’t always know what’s truly achievable. Revenue beyond your wildest dreams? It’ll take time, systems, and trust. But it can undoubtedly be done. Learn more on the latest episode of the #GC

You don’t always know what’s truly achievable. Revenue beyond your wildest dreams? It’ll take time, systems, and trust. But it can undoubtedly be done. Learn more on the latest episode of the #GCAPodcast here: https://t.co/Fsihc0yb5t https://t.co/0mr7tpp5fd

built a claude code plugin that takes screenshots for you and saves them as assets

ok so this has been bugging me for a while. every time im building a portfolio or some landing page where i need to show off features of my app, i go through this whole thing - open the app, take a manual screenshot, rename it to something that makes sense, realize i need webp not png, convert it, then drag it into my public folder. do that like 15 times and you've wasted 30 minutes on something that should take 10 seconds. so i made a claude code plugin called snap-asset that just does all of this for you. you tell claude "screenshot my app and save it as hero" and it actually does it - uses a headless browser, takes a crisp 2x retina capture, converts to both png AND webp, drops it straight into your public/ folder. the cool part imo is the extract mode - you give it any website url and it pulls out everything. the hero section, the navbar, feature cards, footer, all the images. basically rips the whole visual structure into separate optimized assets. been using it to study how other sites lay out their pages. also does component isolation which is kinda sick - point it at a react/vue/svelte component and it spins up a temp vite server, renders just that component with transparent bg, screenshots it, then cleans up. no need to set up storybook just for a screenshot. install: git clone https://github.com/Manavarya09/snap-asset.git ~/.claude/plugins/snap-asset cd ~/.claude/plugins/snap-asset && npm install then just talk to claude normally or use /snap-asset github: https://github.com/Manavarya09/snap-asset submitted by /u/Cheap_Brother1905 [link] [comments]

the right way to build memory. claude is doing it. so are we.

claude's memory architecture got leaked and its smart. here's the same thinking applied with vektori. the Claude Code team purposely(idk :P) shared how their memory system works. the principles are genuinely non obvious and make total sense: memory is an index, not storage. MEMORY.md is just pointers, 150 chars a line. real knowledge lives in separate files fetched on demand. raw transcripts are never loaded only grepped when needed. three layers, each with a different access cost and the sharpest call: if something is derivable, do not store it. retrieval is skeptical. memory is a hint, not truth. the model verifies before using. good architecture. when we started building Vektori that was with the same instincts for a harder problem. the same principles, different shape Claude's three layers are a file hierarchy. bandwidth aware, index always loaded and depth increases cost. Vektori's three layers are a hierarchical sentence graph: FACT LAYER (L0) -- crisp statements. the search surface. cheap, always queryable. | EPISODE LAYER (L1) -- episodes across convos. auto-discovered. | SENTENCE LAYER (L2)-- raw conversation. only fetched when you explicitly need it. same access model. L0 is your index. L2 is your transcript, grepped not dumped. you pay for what you need. strict write discipline too. nothing goes into L0 without passing a quality filter first -- minimum character count, content density check, pronoun ratio. garbage in, garbage out. if a sentence is too vague or purely filler it never becomes a fact. same instinct as Claude not storing derivable things. retrieval works the same way Claude describes: scored, thresholded, skeptical. minimum score of 0.3 before anything surfaces. results are ranked by vector similarity plus temporal decay, not just retrieved blindly. where the architecture diverges is on corrections. Claude's approach is optimized for a single user's project context, where the latest state is usually what matters. agents working across hundreds of sessions need the correction history itself. when a user changes their mind, the old fact stays in the graph with its sentence links. you can always trace back to what was said before the change and why it got superseded. that's the signal most memory systems throw away. we ran this on LongMemEval-S. 73% accuracy at L1 depth with BGE-M3 + Gemini Flash-2.5-lite. multi-hop conflict resolution where you need to reason about how a fact changed over time, is exactly where triple-based systems(subject-object-predicate) collapse. what's next the sentence graph stores what a user said and how it changed. the next layer is storing why. causal edges between events -- "user corrected X, agent updated Y, user disputed again" -- extracted asynchronously and queryable as a graph. agent trajectories as memory. the agent's own behavior becomes part of what it can reason about. same principle as Claude's architecture: structure over storage, retrieval over recall. github.com/vektori-ai/vektori submitted by /u/Expert-Address-2918 [link] [comments]

Are you asking for advice or just want to be agreed with? Good decision-making doesn’t come from opinions. It comes from lived experiences. More on the #GCAPodcast here: https://t.co/kzrfqJUizr http

Are you asking for advice or just want to be agreed with? Good decision-making doesn’t come from opinions. It comes from lived experiences. More on the #GCAPodcast here: https://t.co/kzrfqJUizr https://t.co/RlBUY89C7H

Arguing while negotiating? You’ve already lost. Smartest negotiators use this one trick instead… Find out what it is on the latest episode of the #GCAPodcast here: https://t.co/nPWx0bWGL7 https://t

Arguing while negotiating? You’ve already lost. Smartest negotiators use this one trick instead… Find out what it is on the latest episode of the #GCAPodcast here: https://t.co/nPWx0bWGL7 https://t.co/DbhrsGSDdx

Want to win at negotiating? Change the frame. Instead of arguing the present moment, think: “What would winning look like years from now?” Learn more on the latest episode of the #GCAPodcast here:

Want to win at negotiating? Change the frame. Instead of arguing the present moment, think: “What would winning look like years from now?” Learn more on the latest episode of the #GCAPodcast here: https://t.co/nPWx0bWGL7 https://t.co/mC64S1TRtP

EOD at Crisp HQ. Hiring &amp; Culture Foundations + PREMIER Workshop attendees are already talking through changes for when they get back. Different firms, different challenges, but the same outcome:

EOD at Crisp HQ. Hiring &amp; Culture Foundations + PREMIER Workshop attendees are already talking through changes for when they get back. Different firms, different challenges, but the same outcome: clearer direction on what to fix next. Learn more at https://t.co/EvzbY2AHkR https://t.co/P8tgNUMJbB

Execution is the focus this afternoon in the PREMIER Workshop. Strategy only works if your team can actually carry it out. Learn more at https://t.co/EvzbY2AHkR https://t.co/yWNMegUp0g

Execution is the focus this afternoon in the PREMIER Workshop. Strategy only works if your team can actually carry it out. Learn more at https://t.co/EvzbY2AHkR https://t.co/yWNMegUp0g