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Jev 🤝 CopilotKit We tested UI rendering side by side to demonstrate how Jev from TypeSafe AI can instantly stream components with GPT-6 Luna. Same prompt, renderer and sample data.

CopilotKit🪁

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21,708 次观看 • 5 天前 •via X (Twitter)

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We gave Grok 4.5 and GPT-5.6 Sol the exact same prompt and compared the outputs side by side. The task: generate a fully interactive HTML5 glass bridge simulation with realistic physics, structural failure, cinematic visuals, polished UI, and smooth animations. The difference is actually visible.

We gave Grok 4.5 and GPT-5.6 Sol the exact same prompt and compared the outputs side by side. The task: generate a fully interactive HTML5 glass bridge simulation with realistic physics, structural failure, cinematic visuals, polished UI, and smooth animations. The difference is actually visible.

Entelligence AI

71,177 次观看 • 2 个月前

Jev + GrokBot is the best GTM system I've ever built It just made my GTM x200 CHEAPER and x400 FASTER than what 95% of teams are running... setup takes literally 9 minutes: prompt → GrokBot → Jev routes every candidate → GrokBot opens only survivors → campaign step 1 → open TypeSafe AI, create API key (keep it off chat paste) step 2 → tell GrokBot: store TYPESAFE_API_KEY in the secure field step 3 → prompt GrokBot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → point it at a target: "read end to end, pull positioning, ICP, every proof point. no copy yet" step 5 → add skill gtm-router: every candidate from X / LinkedIn / YouTube gets 6 typed questions → CHEAP SKIP / OPEN FULL / FLAG KOL step 6 → let Jev rank hook archetypes by viral rate, not by how often people post them - this is where the whole playbook comes from step 7 → GrokBot assembles the KOL shortlist and the 7-day plan - you stay in control of anything irreversible the result: → 3,412 candidates, X, LinkedIn and YouTube. → 15.7 seconds. $0.41. → me doing the same reading: 6h 12m. 8 grok bots brief it, Jev decides, GrokBot executes. no fine-tuning, no embeddings, no vector db - 6 typed questions and a schema. Setup your GrokBot JEV GTM today, then read the article below to learn how to build an agent team with JEV.

Jev + GrokBot is the best GTM system I've ever built It just made my GTM x200 CHEAPER and x400 FASTER than what 95% of teams are running... setup takes literally 9 minutes: prompt → GrokBot → Jev routes every candidate → GrokBot opens only survivors → campaign step 1 → open TypeSafe AI, create API key (keep it off chat paste) step 2 → tell GrokBot: store TYPESAFE_API_KEY in the secure field step 3 → prompt GrokBot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → point it at a target: "read end to end, pull positioning, ICP, every proof point. no copy yet" step 5 → add skill gtm-router: every candidate from X / LinkedIn / YouTube gets 6 typed questions → CHEAP SKIP / OPEN FULL / FLAG KOL step 6 → let Jev rank hook archetypes by viral rate, not by how often people post them - this is where the whole playbook comes from step 7 → GrokBot assembles the KOL shortlist and the 7-day plan - you stay in control of anything irreversible the result: → 3,412 candidates, X, LinkedIn and YouTube. → 15.7 seconds. $0.41. → me doing the same reading: 6h 12m. 8 grok bots brief it, Jev decides, GrokBot executes. no fine-tuning, no embeddings, no vector db - 6 typed questions and a schema. Setup your GrokBot JEV GTM today, then read the article below to learn how to build an agent team with JEV.

Movez

85,453 次观看 • 11 天前

Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open TypeSafe AI , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓

Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open TypeSafe AI , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓

codila

381,667 次观看 • 11 天前

Opus 5.5 + GrokBot + Jev... I can't believe that I build it I packed the best of AI into ONE agent - now 99% of people’s AI agents look dumb, slow, and expensive Try here (free): prompt → GrokBot → Jev chooses LLM → Claude / GPT working on GrokBot's computer 24/7 → result → Jev sends only the tasks Grok is weak at to Opus or GPT → Jev picks the best model for each task - for example, Claude for motion design and Codex for web design → runs on the ChatGPT and Claude subscriptions you already pay for (API also work) → works on GrokBot computer while you keep chatting → checks every result against the goal before you see it → GrokBot gets smarter and cheaper at the same time → And plenty more features in a bot everyone will call "obvious" by 2030 Try Jevopus, thank me later - then read the article on the full Jev architecture ↓

Opus 5.5 + GrokBot + Jev... I can't believe that I build it I packed the best of AI into ONE agent - now 99% of people’s AI agents look dumb, slow, and expensive Try here (free): prompt → GrokBot → Jev chooses LLM → Claude / GPT working on GrokBot's computer 24/7 → result → Jev sends only the tasks Grok is weak at to Opus or GPT → Jev picks the best model for each task - for example, Claude for motion design and Codex for web design → runs on the ChatGPT and Claude subscriptions you already pay for (API also work) → works on GrokBot computer while you keep chatting → checks every result against the goal before you see it → GrokBot gets smarter and cheaper at the same time → And plenty more features in a bot everyone will call "obvious" by 2030 Try Jevopus, thank me later - then read the article on the full Jev architecture ↓

codila

254,695 次观看 • 3 天前

Want to generate images from the world's best models like Nano Banana or GPT Image side by side? Now you can with Image Lab 🚀 Run a single prompt across up to 25 models, with up to 20 images from each, and see results in seconds. You've seen our leaderboards. Now generate and evaluate the models yourself. Link in thread 🧵👇

Want to generate images from the world's best models like Nano Banana or GPT Image side by side? Now you can with Image Lab 🚀 Run a single prompt across up to 25 models, with up to 20 images from each, and see results in seconds. You've seen our leaderboards. Now generate and evaluate the models yourself. Link in thread 🧵👇

Artificial Analysis

12,250 次观看 • 7 个月前

Can you tell the difference? I tested the exact same prompt, same scene, and identical luxury perfume commercial in Seedance 2.0 4k on EaseMate AI_Official , comparing 4K and 1080p output. From the Mediterranean lighting and glass reflections to skin texture and cinematic depth of field, the difference is clear when viewed side by side. Which version would you choose for your next commercial—4K or 1080p? 👇 Prompt in the comments.

Can you tell the difference? I tested the exact same prompt, same scene, and identical luxury perfume commercial in Seedance 2.0 4k on EaseMate AI_Official , comparing 4K and 1080p output. From the Mediterranean lighting and glass reflections to skin texture and cinematic depth of field, the difference is clear when viewed side by side. Which version would you choose for your next commercial—4K or 1080p? 👇 Prompt in the comments.

Jessica Collins

468,180 次观看 • 2 个月前

You can now classify documents with open-weight VLMs at ultra-low cost with our TypeSafe AI Jev-compatible API in the VLM Run gateway! $1 buys you 6700+ pages, and the full 1,651-page OmniDocBench run cost 25 cents. It reads the page as pixels (no OCR->LLM hacks) and hands back one typed, calibrated decision per page - ~180ms p50 e2e. Jev really opened developers' eyes to re-thinking how we process data at the cost-pareto front. A week later we're bringing that to our APIs with vision as a first-class citizen. Try our gateway today - free to sign-up and try!

You can now classify documents with open-weight VLMs at ultra-low cost with our TypeSafe AI Jev-compatible API in the VLM Run gateway! $1 buys you 6700+ pages, and the full 1,651-page OmniDocBench run cost 25 cents. It reads the page as pixels (no OCR->LLM hacks) and hands back one typed, calibrated decision per page - ~180ms p50 e2e. Jev really opened developers' eyes to re-thinking how we process data at the cost-pareto front. A week later we're bringing that to our APIs with vision as a first-class citizen. Try our gateway today - free to sign-up and try!

Sudeep Pillai

19,735 次观看 • 3 天前

$Jev + Muse is the first AI agent system that actually automate 100% of my life 99% of people pay 200x more for slower AI agents - while 1% run this 2030 setup just 5 min and setup is ready: prompt → Muse → Jev decision → Muse execution → result step 1 → create your Jev API key (typesafe website) step 2 → clone and install the complete router from Github below python3 -m venv .venv && .venv/bin/pip install -r requirements.txt && cp config.example.yaml config.yaml step 3 → export the key before running anything: export TYPESAFE_API_KEY='YOUR_KEY' add the same export to ~/.zshrc or ~/.bashrc if you want it to survive a new terminal session step 4 → give your agent skill/jev-decision-layer.SKILL.md and connect it to src/router.py + recipes/ , raw Jev returns probabilities - the router converts them into executable actions step 5 → test the entire chain, not the raw Jev API: .venv/bin/python -m src.cli '{"goal":"what is 2+2?","kind":"chat"}' the final JSON should contain action, reason, mode, jev_used and confidence details step 6 → keep mode: shadow for 20–50 real decisions: the agent works normally while Jev’s routes are logged and checked; promote only reliable question packs step 7 → switch to mode: active with hard confidence gates: ≥0.80 act automatically, 0.50–0.79 advisory only, <0.50 escalate to the human the result: Jev + Muse is a system that decides what to do, what to skip and when to bring in - I’ve tested it across my daily workflows, and it’s the best setup I’ve found for automating routine Take the exact stack I built, run it yourself from the repo - then read the full Jev architecture behind it ↓$

Jev + Muse is the first AI agent system that actually automate 100% of my life 99% of people pay 200x more for slower AI agents - while 1% run this 2030 setup just 5 min and setup is ready: prompt → Muse → Jev decision → Muse execution → result step 1 → create your Jev API key (typesafe website) step 2 → clone and install the complete router from Github below python3 -m venv .venv && .venv/bin/pip install -r requirements.txt && cp config.example.yaml config.yaml step 3 → export the key before running anything: export TYPESAFE_API_KEY='YOUR_KEY' add the same export to ~/.zshrc or ~/.bashrc if you want it to survive a new terminal session step 4 → give your agent skill/jev-decision-layer.SKILL.md and connect it to src/router.py + recipes/ , raw Jev returns probabilities - the router converts them into executable actions step 5 → test the entire chain, not the raw Jev API: .venv/bin/python -m src.cli '{"goal":"what is 2+2?","kind":"chat"}' the final JSON should contain action, reason, mode, jev_used and confidence details step 6 → keep mode: shadow for 20–50 real decisions: the agent works normally while Jev’s routes are logged and checked; promote only reliable question packs step 7 → switch to mode: active with hard confidence gates: ≥0.80 act automatically, 0.50–0.79 advisory only, <0.50 escalate to the human the result: Jev + Muse is a system that decides what to do, what to skip and when to bring in - I’ve tested it across my daily workflows, and it’s the best setup I’ve found for automating routine Take the exact stack I built, run it yourself from the repo - then read the full Jev architecture behind it ↓

codila

91,306 次观看 • 9 天前

We replaced an entire 100k/month creative strategy team with an AI agent that sits in slack. I tested it side by side against our internal team and it blew them away. Here’s what it can do + 5 prompts you should steal (bookmark this)

We replaced an entire 100k/month creative strategy team with an AI agent that sits in slack. I tested it side by side against our internal team and it blew them away. Here’s what it can do + 5 prompts you should steal (bookmark this)

Arie Scherson

13,253 次观看 • 8 个月前

Seedance 2.5 is officially available on CapCut. To test it, I used the same cinematic prompt in Seedance 2.5 and Seedance 2.0 and compared the results side by side. With support for up to 30-second generation, 50 reference materials, infinite scene extension, and frame-precise editing, Seedance 2.5 gives creators much more control over complex cinematic videos. Same prompt. Same settings. Watch both generations and see how they compare in prompt accuracy, motion quality, and cinematic consistency.

Seedance 2.5 is officially available on CapCut. To test it, I used the same cinematic prompt in Seedance 2.5 and Seedance 2.0 and compared the results side by side. With support for up to 30-second generation, 50 reference materials, infinite scene extension, and frame-precise editing, Seedance 2.5 gives creators much more control over complex cinematic videos. Same prompt. Same settings. Watch both generations and see how they compare in prompt accuracy, motion quality, and cinematic consistency.

Zara

24,291 次观看 • 2 个月前

We gave Jev 2,029 real phone calls. No transcripts or audio; it never heard a word. Our AI receptionist's calls were reduced to pure structure, meaning turns, tool calls, workflow stages and timing. During the calls, Jev made 38,012 turn-level forecasts at 118 ms median latency, reviewed every call with five typed questions and produced 10,145 answers in 26 seconds with 256 requests in flight. The experiment was zero-shot, with no fine-tuning or examples from our data. We compared Jev's forecasts with what actually happened in the EHR. By the halfway point, Jev could meaningfully separate calls that would book from those that wouldn't (AUC 0.78), and near the end it ranked them correctly 94% of the time. Even though Jev over-focused on visible errors our agent usually overcomes, it's still pretty incredible that it analyzed thousands of real calls in seconds for only $3.

We gave Jev 2,029 real phone calls. No transcripts or audio; it never heard a word. Our AI receptionist's calls were reduced to pure structure, meaning turns, tool calls, workflow stages and timing. During the calls, Jev made 38,012 turn-level forecasts at 118 ms median latency, reviewed every call with five typed questions and produced 10,145 answers in 26 seconds with 256 requests in flight. The experiment was zero-shot, with no fine-tuning or examples from our data. We compared Jev's forecasts with what actually happened in the EHR. By the halfway point, Jev could meaningfully separate calls that would book from those that wouldn't (AUC 0.78), and near the end it ranked them correctly 94% of the time. Even though Jev over-focused on visible errors our agent usually overcomes, it's still pretty incredible that it analyzed thousands of real calls in seconds for only $3.

Muratcan Koylan

354,294 次观看 • 2 天前

Found a great production use case for Jev. I used Jev to organize ~2.3K AI research papers. The total cost was $0.14, and it took about 83 seconds. The process: The papers already had old tags, which I ran through a previous open model (DeepSeek V4 Flash). However, I wasn't confident in the classifications, and I didn't want to spend more on tokens unless I spent time tuning it into a good LLM classifier via few-shot (more expensive). Too tedious, too costly, and unsustainable. Luckily for us, we now have Jev to help us with organizing papers better. So Jev first went through all the papers and retagged them. It agreed with 75% of the previous tags. Jev found about 579 high-confidence topic changes. I evaluated reliability by manually labeling 30 disagreements and accepted all of them. I was astonished by Jev's classification capabilities. We applied and verified all changes in production. I'm much more satisfied with the classifications, but I think there is still room for improvement. Check out the papers here: I will experiment with Jev more. The takeaway is that pipelines can be significantly improved by carefully combining System One and System Two models. Jev clearly unlocks more interesting ways to organize papers and offer a more useful discovery layer for research papers. More updates on that soon.

Found a great production use case for Jev. I used Jev to organize ~2.3K AI research papers. The total cost was $0.14, and it took about 83 seconds. The process: The papers already had old tags, which I ran through a previous open model (DeepSeek V4 Flash). However, I wasn't confident in the classifications, and I didn't want to spend more on tokens unless I spent time tuning it into a good LLM classifier via few-shot (more expensive). Too tedious, too costly, and unsustainable. Luckily for us, we now have Jev to help us with organizing papers better. So Jev first went through all the papers and retagged them. It agreed with 75% of the previous tags. Jev found about 579 high-confidence topic changes. I evaluated reliability by manually labeling 30 disagreements and accepted all of them. I was astonished by Jev's classification capabilities. We applied and verified all changes in production. I'm much more satisfied with the classifications, but I think there is still room for improvement. Check out the papers here: I will experiment with Jev more. The takeaway is that pipelines can be significantly improved by carefully combining System One and System Two models. Jev clearly unlocks more interesting ways to organize papers and offer a more useful discovery layer for research papers. More updates on that soon.

elvis

15,427 次观看 • 10 天前

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.

Armagan Amcalar

365,108 次观看 • 8 天前

You can now use Jev right inside Claude Code 🤯 It's called jev-model-router, an early access mod built on Claude Code's new function hooks. Before every turn, it checks in with Jev and asks: > how mechanical the task is > how much reasoning it needs > whether it's risky Then it routes: → it'll move up to a stronger model on weak evidence, and only drops to a cheaper one when it's confident the task is simple → every decision gets logged in your transcript → if the call fails, your request runs untouched Setup: 1. copy the install command: npx claude-code-templates@latest --mod productivity/jev-model-router 2. paste it inside Claude Code 3. run claude with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 set 4. accept the trust prompt on first launch Link: Also works with no api key, it just falls back to a built-in classifier with no confidence score. For real jev routing, add your typesafeApiKey or gatewayApiKey to ~/.claude/settings.json. Follow me for more AI workflows and tutorials.

You can now use Jev right inside Claude Code 🤯 It's called jev-model-router, an early access mod built on Claude Code's new function hooks. Before every turn, it checks in with Jev and asks: > how mechanical the task is > how much reasoning it needs > whether it's risky Then it routes: → it'll move up to a stronger model on weak evidence, and only drops to a cheaper one when it's confident the task is simple → every decision gets logged in your transcript → if the call fails, your request runs untouched Setup: 1. copy the install command: npx claude-code-templates@latest --mod productivity/jev-model-router 2. paste it inside Claude Code 3. run claude with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 set 4. accept the trust prompt on first launch Link: Also works with no api key, it just falls back to a built-in classifier with no confidence score. For real jev routing, add your typesafeApiKey or gatewayApiKey to ~/.claude/settings.json. Follow me for more AI workflows and tutorials.

Alvaro Cintas

42,484 次观看 • 9 天前

Performance gains in bazza/ui vs. Base UI Finally did a side-by-side comparison... and the difference is ⚡ You can see how our implementation barely drops submenus opening when moving the cursor up/down at a fast speed. The video doesn't do it (enough) justice. We do a few things internally to make this happen -- excited to release this to the world. Soon.

Performance gains in bazza/ui vs. Base UI Finally did a side-by-side comparison... and the difference is ⚡ You can see how our implementation barely drops submenus opening when moving the cursor up/down at a fast speed. The video doesn't do it (enough) justice. We do a few things internally to make this happen -- excited to release this to the world. Soon.

kian bazza

137,476 次观看 • 7 个月前

Sakana Fugu surprisingly performed near GLM 5.2 level but 17× more expensive! We gave the same prompt to 4 models: build a complete live Trader Desk with both frontend and backend components, real-time market data fetched from external APIs for 8 symbols, and a custom dark-theme UI. Outputs: Fugu Ultra — 22,225 t, $0.51 Opus 4.8 — 15,802 t, $0.31 GPT-5.5 — 11,474 t, $0.26 GLM 5.2 — 13,677 t, $0.03 Fugu created the most polished and feature-rich trading desk in the run. GLM 5.2 was very close behind, with a similarly complete multi-panel interface and live data, but at a much lower cost. Opus and GPT also performed well, delivering solid results with a better balance between quality and cost

Sakana Fugu surprisingly performed near GLM 5.2 level but 17× more expensive! We gave the same prompt to 4 models: build a complete live Trader Desk with both frontend and backend components, real-time market data fetched from external APIs for 8 symbols, and a custom dark-theme UI. Outputs: Fugu Ultra — 22,225 t, $0.51 Opus 4.8 — 15,802 t, $0.31 GPT-5.5 — 11,474 t, $0.26 GLM 5.2 — 13,677 t, $0.03 Fugu created the most polished and feature-rich trading desk in the run. GLM 5.2 was very close behind, with a similarly complete multi-panel interface and live data, but at a much lower cost. Opus and GPT also performed well, delivering solid results with a better balance between quality and cost

atomic.chat

745,298 次观看 • 3 个月前

Sep 30 is **HYPERSHIP** day on Product Hunt. If you think you can build and ship fast, and you haven't launched recently, submit your launch by midnight Sep 30. Be prepared to build and ship multiple features in real-time. Appropriately, given the speed theme, we’ve partnered with TypeSafe AI, makers of Jev, a frontier model at real-time speed, and Supabase, the backend platform you know and love! Prizes will include Jev and Supabase credits, swag boxes, and secret prizes we’ll announce later. And if you're not launching: Mark your calendars to try new products, make feature requests, and watch the products evolve over the course of the day. This is the day where we redefine what shipping velocity means.

Sep 30 is HYPERSHIP day on Product Hunt. If you think you can build and ship fast, and you haven't launched recently, submit your launch by midnight Sep 30. Be prepared to build and ship multiple features in real-time. Appropriately, given the speed theme, we’ve partnered with TypeSafe AI, makers of Jev, a frontier model at real-time speed, and Supabase, the backend platform you know and love! Prizes will include Jev and Supabase credits, swag boxes, and secret prizes we’ll announce later. And if you're not launching: Mark your calendars to try new products, make feature requests, and watch the products evolve over the course of the day. This is the day where we redefine what shipping velocity means.

Product Hunt 😸

51,144 次观看 • 7 天前

Hiring managers are still spending 10–15 hours manually screening resumes. AI can do it in minutes. I tested it with one prompt: “Review 200 resumes against this job description. Rank the top 10 and explain why.” Here’s what came back: • A ranked shortlist with match scores • Clear, evidence-based reasoning for every candidate • Red flags highlighted with context • A side-by-side comparison of the top contenders • Custom interview questions tailored to each profile No fluff. No guesswork. Just structured, defensible hiring insights—fast. This is what modern recruiting looks like.

CT

38,053 次观看 • 5 个月前