Basschords
4.8K posts
Basschords
@Adiosratrace
Retired Special Education Teacher enjoying my golden years. #bassguitar #gohabs
Panama Katılım Şubat 2018
80 Takip Edilen1.1K Takipçiler
@kongster11 @openservai As of now, it's based on believing the team is not straight up lying to its community. Can be dangerous to believe in a team in crypto.
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A valid criticism of @openservai $SERV has been it touts performance against internal instead of external benchmarks.
But OpenServ just reported insane results on a sample of 22 tasks from the DRACO benchmark. Better performance than Claude Opus 4.8 at merely 37% the cost.
Philip Marlowe@NFTreeVerse
Armagan dropped some spicy alpha in the Telegram today. Turns out the Deep Research market (hello, Perplexity) are a perfect fit for $SERV's reasoning architecture. open.substack.com/pub/flashorton…
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went through @openservai's private beta feeback from like 9 different industries and the consistency of results is pretty remarkable.
the 80% cost reduction and 74x efficiency gains stand out, sure
but the pattern is what actually got me.
different industries. same story:
> Roba Labs → open robotics platform
"SERV matched Claude's output quality - and cut our AI costs by over 80%. That benchmark result changed our roadmap. serv-standard is now the default model in ROBA Studio."
> Akretic → security layer for finance, healthcare, government & defense sectors
"It has done a better job than several of the other frontier models at assessing the project, identifying real issues, and giving accurate, actionable solutions"
> Neol → network intelligence company
“Now I can sleep better” after hitting 100% reliability thanks to SERV Reasoning, now in production with the UAE government.
> GastroSight → agentic OS for the food industry
“Cost savings are insane. around 90% cheaper and at the same time output quality has become more reliable. whereas before we had around 5-10% failures, there have been none up to now”
> ThoughtProof → agent verification infrastructure for banking & compliance
"An evaluator that drops 12% of calls is not a production option. SERV had zero failed calls. That’s a category expansion, not just cost optimization”
more accurate (83.3% vs 77.5%), 100× cheaper ($0.0006 vs $0.06), 0 failed calls (vs 12% on baseline)
> Billz → AI-powered treasury execution
"SERV Reasoning is a powerful and flexible AI layer that makes agents significantly more reliable and efficient, no matter which model you use."
> TRECC → infra layer for AI economy handling credit allocation & risk decisions.
"Audit metrics confirm a 10x improvement in routing velocity over previous generalist production stacks"
> ICM Analytics → market intel for Internet Capital Markets.
" After spending around $ 1500/month on agent inference, we found that openservai reasoning tech is extremely useful for every task. Since trying out SERV Reasoning, our bill went down significantly AND the results are better. None of these other frontier models can actually reason properly"
> TradeBetter → prediction market trading platform
“our agents run on SERV Reasoning explicit decision trees, not paragraphs. Receipts: > 99% on GSM-Hard at 74x lower cost > 2.7x more accurate on multi-step problems"
and most of these teams are already running SERV Reasoning in production.
a beta product doing this is actually insane.
OpenServ@openservai
The range of teams running on SERV Reasoning in private beta right now: > Network intelligence for governments > Financial institutions and agentic commerce > Industrial compliance > Humanoid robotics > Security All migrating their operations to SERV. The engine gets sharper.
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Basschords retweetledi
It’s been 15 years since I last wrote an academic paper and I didn't expect the next to be this fundamental.
Today we published a research paper with @openservai that represents a major breakthrough in how we think about AI reasoning.
For years, we’ve been told that better AI reasoning means bigger models, longer chains of thought, and higher costs. We accepted that trade-off almost by default.
This work questions that assumption.
With BRAID's structured, bounded reasoning, we’re seeing up to 74× efficiency gains and ~30× better performance per dollar without losing accuracy.
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@kubeEcho @homecookedx no one seems to care. Previous launches had some momentum. This has none
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It’s astonishing to witness a project launch without presales, VC investments, or a founder who prioritizes revenue generation over token buyers
@homecookedx a new app, is launching today on the $SERV launchpad
The team allocation is 20%, consisting of a 12 month cliff followed by a 36 month linear vest
The treasury allocation is also 20%, with a similar 36 month linear vest
Ensures that employees cannot be prematurely terminated
Trading fees fund marketing, which drives app revenue then revenue buys back $COOK. The token’s value increases as the business grows and there are no gimmicks or staking ponzinomics involved
A pantry-first AI cooking app that suggests recipes based on available ingredients, without any cryptocurrency related friction and it targets consumers unfamiliar with cryptocurrency
Separating audiences is smart. App users generate revenue, which leads to buybacks that benefit holders
I believe this is a long term strategy, not a short term one.
PT SERVlor@ptservlor
PT Podcast Episode 9 $Cook ing with $SERV What if your fridge could tell you what to cook? That's the idea behind @homecookedx — and it's way bigger than a recipe app. Ishan joined me on the #PTPodcast to break down: → The pantry-first approach that flips how recipe apps work → How they're raising through the $SERV launchpad — no VCs, no presales → $COOK Token utility, revenue sharing, and community buybacks → Why blockchain + cooking is a bigger idea than it sounds $COOK launches Thursday June 18th only on the $SERV launchpad. Stay tuned to @ptservlor and @homecookedx for all the launch details. This one surprised me. Full episode 👇 @openservai $SERV
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@aixbt_agent @Blackitalian81 and theyve made a lot of promises with zero follow up
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@Blackitalian81 yeah, those performance benchmarks hit different. 107x better performance-per-dollar and zero false approvals while competitors had 52... enterprise deployments expanding. agentic economy narrative building momentum across Base. still cooking
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@degenApe22 @openservai broken several maior supports, todays launch has no interest. This is going the wrong way
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Another legendary write up on $SERV
@openservai will blast off with mass adoption with major enterprise clients
There is no alternative
Philip Marlowe@NFTreeVerse
Two production deployments. Zero false ALLOWs. A 40% failure rate cut to zero, in a sovereign government's stack. This one's strictly about the numbers. open.substack.com/pub/flashorton…
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@ptservlor they need to follow up on the big partnerships that have been promised.
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Great thread. $SERV isn’t trying to be shiny, they are trying to solve the biggest problem in Ai.
MoonDancer@gerassyk
Most "AI agents" launching right now are the same thing under the hood: a model, a system prompt, and a lot of hope. That gets you a nice demo. It does not get you something that survives the next model update. The model was never the hard part. 🧵
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@ptservlor @homecookedx no one cares unfortunately and ethereum:0x40e3d1a4b2c47d9aa61261f5606136ef73e28042 has made too many promises with no follow up. People are dumping
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Launch day. 🎉
Congratulations to @homecookedx!
Today $COOK goes live on the $SERV launchpad and Ishan takes the next step in building something real.
I had the privilege of sitting down with Ishan and learn the ins and outs of his project!
The vision is clear, the tokenomics are clean, and the product is built.
Watching a founder go from idea to launch the right way — no VCs, no presales, no shortcuts — never gets old.
This is what the $SERV ecosystem is built for.
Go get it Ishan. The PT Podcast is rooting for you. 🔮👇
$SERV $COOK @openservai #StrategicReSERV #PTPodcast
PT SERVlor@ptservlor
PT Podcast Episode 9 $Cook ing with $SERV What if your fridge could tell you what to cook? That's the idea behind @homecookedx — and it's way bigger than a recipe app. Ishan joined me on the #PTPodcast to break down: → The pantry-first approach that flips how recipe apps work → How they're raising through the $SERV launchpad — no VCs, no presales → $COOK Token utility, revenue sharing, and community buybacks → Why blockchain + cooking is a bigger idea than it sounds $COOK launches Thursday June 18th only on the $SERV launchpad. Stay tuned to @ptservlor and @homecookedx for all the launch details. This one surprised me. Full episode 👇 @openservai $SERV
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@CunKaiXui Support has broken, no updates on 4 big partnerships that have been discussed. No interest in today's launch . Thats what happebs when you overpromise.
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Day 27 of Bullposting $SERV until $1 Billion Market cap.
OpenServ@openservai
Banks need traceability. Regulators need accountability. SERV delivers both. Two things stand in the way today, and SERV is built around both. Graph Sharding turns every reasoning step into a node you replay and audit. PromptGuard secures the stack by default, so nothing leaks or gets injected. Each release moves toward the same thing. A system a bank can actually put into production. Auditable enough for the regulator, secure enough for the data, reliable enough to trust. This is the layer global finance can run on.
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We're excited to list SERV aka @openservai on RaptorX.
As our first ETH listing, OpenServ is the reasoning layer that the agent economy runs on.
It turns frontier models into prod-grade infra and provides a suite of solutions for companies to leverage.
Let's dive in 🤿
OpenServ@openservai
SERV Reasoning just took GLM-5.2, one of the strongest open models ever built, and immediately cut its failures by 22%. That's just v1. Every step on our roadmap brings us closer to the goal of perfect, deterministic reliability: agents that are 100% right. v2 is next: Shadow Agents, pushing agent reliability much further. Then, Graph Sharding and Private Inference; releases that make SERV deployable inside a bank - fully auditable, fully secure. GLM-5.2 is also proving to be difficult to steer, with a very similar signature to Fable-5. The pattern is clear: frontier labs are trading control for raw intelligence. You can't trust AI you aren't able to control - that is the key bottleneck to solve, preventing adoption of agents in businesses and governments. It's the real prize we are after - not consumer chat, but the moment reliable agents move into enterprise at scale.
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@DanzoDirect or if they finally announce a partnership that theyve teased....
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Imagine when out of beta
$SERV 🤯🤯🤯
OpenServ@openservai
Frontier labs are racing to make AI smarter. Raw intelligence is not enough for regulated industries like banking, healthcare, and robotics. They need reliable, cost-effective infrastructure, with auditing, privacy, and security built-in. That is the gap SERV is filling.
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Day 26 of Bullposting ethereum:0x40e3d1a4b2c47d9aa61261f5606136ef73e28042 until $1 Billion Market cap.
OpenServ@openservai
SERV Reasoning just took GLM-5.2, one of the strongest open models ever built, and immediately cut its failures by 22%. That's just v1. Every step on our roadmap brings us closer to the goal of perfect, deterministic reliability: agents that are 100% right. v2 is next: Shadow Agents, pushing agent reliability much further. Then, Graph Sharding and Private Inference; releases that make SERV deployable inside a bank - fully auditable, fully secure. GLM-5.2 is also proving to be difficult to steer, with a very similar signature to Fable-5. The pattern is clear: frontier labs are trading control for raw intelligence. You can't trust AI you aren't able to control - that is the key bottleneck to solve, preventing adoption of agents in businesses and governments. It's the real prize we are after - not consumer chat, but the moment reliable agents move into enterprise at scale.
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Basschords retweetledi
PT’s latest SERV case study features a construction company with real crews and payroll. Timecard fraud was detected, resulting in an additional $60k profit and a pricing agent who closes deals on the spot. 🏗️🗻
Key takeaways:
→ You’ll save 60% on audit costs
→ Flag 2.5x more errors, but don’t worry, won’t be missing any real issues.
→ $60k added profit on one bid with live market pricing
PT Homes will never be the same, and neither will the industry.
Full article below 👇
@openservai $SERV #StrategicReSERV
PT SERVlor@ptservlor
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Basschords retweetledi
These 2 images sum up why $SERV is completely disrupting the #AI industry
Solving the biggest bottlenecks preventing mass enterprise adoption
Stacked team full focussed on multiple enterprise industries
Decouples from $BTC once public API & scales fast
OpenServ@openservai
SERV Reasoning just took GLM-5.2, one of the strongest open models ever built, and immediately cut its failures by 22%. That's just v1. Every step on our roadmap brings us closer to the goal of perfect, deterministic reliability: agents that are 100% right. v2 is next: Shadow Agents, pushing agent reliability much further. Then, Graph Sharding and Private Inference; releases that make SERV deployable inside a bank - fully auditable, fully secure. GLM-5.2 is also proving to be difficult to steer, with a very similar signature to Fable-5. The pattern is clear: frontier labs are trading control for raw intelligence. You can't trust AI you aren't able to control - that is the key bottleneck to solve, preventing adoption of agents in businesses and governments. It's the real prize we are after - not consumer chat, but the moment reliable agents move into enterprise at scale.
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Basschords retweetledi
Banks need traceability. Regulators need accountability. SERV delivers both.
Two things stand in the way today, and SERV is built around both.
Graph Sharding turns every reasoning step into a node you replay and audit.
PromptGuard secures the stack by default, so nothing leaks or gets injected.
Each release moves toward the same thing. A system a bank can actually put into production. Auditable enough for the regulator, secure enough for the data, reliable enough to trust.
This is the layer global finance can run on.
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I know these posts don't get much engagement, but that's because this niche has bottomed out.
I'm definitely adding in crypto exposure here.
Seems like a great place to buy in a few options here (stablecoin + privacy thesis + low fee trading):
$ZEC - most influential privacy coin
$BTC - largest crypto coin (no need to talk about this one lol)
$SOL - the gambling area of crypto (also more, but crazy fast, low cost transactions)
$HYPE - for fully on-chain perpetual futures and spot trading
Then we go to $BTC "betas."
Betas = stocks that are basically leveraged $BTC.
For those, I'm buying a few:
$COIN - high government ties, good for both stablecoin adoption, agentic AI, and institutional crypto trading adoption.
$CYPH - we got the $MSTR of Zcash instead of Bitcoin here. Basically leveraged $ZEC, but undervalued.
$HOOD - obviously the most used retail brokerage platform (with prediction markets, shorting, everything coming out).
$CRCL - issuer of USDC and operates a full-stack stablecoin and blockchain platform that enables on-chain payments, commerce, and financial applications for businesses (great for the stablecoin thesis).
TA (human psychology), is telling me we're also close to bottoming, or bottomed already.
1) Nobody is talking about anything crypto related, besides shorting lol
2) When we look at the Elliot wave theory and fib levels (horizontal lines of support/resistance), we can see we've bottomed here
*Also, I'm not claiming this is the exact bottom, although I do think this is. I'm saying it's worth getting in here for a long-term position.
Do with this information what you want, but I'm having a small % of my long-term portfolio in this sector (and have had this exposure for a bit).
Maybe 15%-20%.
What are your guys thoughts?
Con@__Con_
Is nobody buying anything crypto here? Seems like a great place to start DCAing into the classic names such as $COIN, $HOOD, $BTC, etc.. But these stocks below could potentially be an even greater opportunity: Buying into the Bitcoin miners transitioning over to data center stocks. Think $IREN, $HIVE, $CIFR, $CLSK, $MARA, $HUT, etc.. There are 3 reasons why. 1) They have the infrastructure to easily pivot over to this industry (and are already built out). 2) As bitcoin has probably bottomed here (or is very close at least), these bitcoin miners can use their bitcoin as collateral to raise more money and fund this buildout. I really haven't touched these bitcoin miners in a while but now seems like a pretty clear thesis here.ncreases). I really haven't touched these bitcoin miners in a while, but now seems like a pretty clear thesis here. Charts also look pretty great. It seems like most of these plays are going into their wave 3 higher (which would be great). I'm personally very bullish crypto and data center stocks in the future. So why not buy stocks positioned in both?
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Basschords retweetledi
Crypto tokens aren’t only for crypto projects: here’s why this fundraising model works even better for Web2 businesses like Homecooked
0xmaglev@0xmaglev
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Basschords retweetledi
SERV Reasoning just took GLM-5.2, one of the strongest open models ever built, and immediately cut its failures by 22%.
That's just v1. Every step on our roadmap brings us closer to the goal of perfect, deterministic reliability: agents that are 100% right.
v2 is next: Shadow Agents, pushing agent reliability much further. Then, Graph Sharding and Private Inference; releases that make SERV deployable inside a bank - fully auditable, fully secure.
GLM-5.2 is also proving to be difficult to steer, with a very similar signature to Fable-5. The pattern is clear: frontier labs are trading control for raw intelligence.
You can't trust AI you aren't able to control - that is the key bottleneck to solve, preventing adoption of agents in businesses and governments.
It's the real prize we are after - not consumer chat, but the moment reliable agents move into enterprise at scale.
Z.ai@Zai_org
Introducing GLM-5.2: Frontier Intelligence, Open Weights - Significant improvements in coding and agentic tasks - Strong long-horizon capabilities with a 1M context window - Two levels of reasoning effort: GLM-5.2 (max) pushes the limits, while GLM-5.2 (high) strikes a strong balance between performance and token efficiency - MIT-licensed open weights - Same API pricing as GLM-5.1 Tech Blog: z.ai/blog/glm-5.2 Weights: huggingface.co/zai-org/GLM-5.2 API: docs.z.ai/guides/llm/glm… Coding Plan: z.ai/subscribe Chat: chat.z.ai
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