Private and encrypted. Maple builds your product, audits your code, and scans for vulnerabilities. Privacy by design.
Private AI →
Joined February 2025
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New on Maple Max and Team: DeepSeek V4.1 Flash, abliterated.
Refusals removed from the model itself, so it works through security questions instead of declining them.
- 1M context
- Runs in the same attested enclaves as every Maple model
- Built for security research, code audits and red-teaming
Attackers already run uncensored models. Defenders shouldn't be stuck with "I can't help with that."
Maple AI retweeted
Going live on Bitcoin Magazine's main stream in a few minutes.
LIVE: MSCI & Strategy, Post-Midterm Crypto Politics, The $40T Debt Trap | BMTV Nov. 12, 2026 xitter.twitterapi.xyz/i/broadcasts/2dKRRbXkE…
Maple AI retweeted
The AI Crash Last Week Was Sniffing Out Something BIG xitter.twitterapi.xyz/i/broadcasts/2AMPPoEJa…
Big move from Google. Chips built so even an insider can't access your data while AI runs.
This should be the standard. Maple already works this way, and you can verify it yourself.
Today, we're expanding our eighth-generation TPU portfolio with two new systems built specifically for the agentic era — bringing our total to four distinct eighth-generation TPU systems launched in a single year.
TPU 8i Max was built for massive context windows and the highest compute density per rack, and TPU 8tx is optimized for secure and confidential inference. While flexible across workloads, the four systems in our 8th gen TPU portfolio give developers and organizations a purpose-built accelerator for every stage of the agentic lifecycle.
@AminVahdat, SVP and Chief Technologist, AI and Infrastructure, explains why we're expanding our TPU portfolio and what everyone needs to know about TPU 8tx and 8i Max ⬇️
Maple AI retweeted
Cyberattackers don't ask for permission.
Defenders shouldn't be met with "I can't help with that."
Your washing machine isn't on Wi-Fi.
You decline tracking cookies.
You cover your webcam.
You use a VPN.
Then you hand your whole computer to an AI agent whose privacy policy keeps changing.
It doesn't have to be like this anymore.
Maple Work is an AI agent for your desktop. Private by design.
Early access rolling out now.
Thank you Maple, thanks to you I can allocate some of my screen time to ask DeepSeek V4.1 Uncensored how to break out of here!!
Also the cake was delicious.
- The guards
We baked Felix a cake.
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Get private superintelligence in your pocket.
10% off your first 3 months of Maple with code FREEFELIX
maple.ai/pricing?utm_source=…
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An open-weight model within a point of Claude Opus 5.5 on agentic coding is now in Maple.
Kimi K3.1 Pro from @Kimi_Moonshot, running in attested secure enclaves, so no third party can read your chats.
In Maple Chat and Maple Work, on Pro, Max and Team.
Near-frontier. Open weights. Private.
What changed from K3, what it takes to run it yourself, and how to use it in Maple 👇
maple.ai/blog/kimi-k3-1-pro-…
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Thanks @TinfoilAI for getting Kimi K3.1 Pro running in secure enclaves so quickly after the weights dropped 🩶 It's live in Maple now.
securing compute as an enterprise was a stupid consideration a year ago
OS models just weren’t good enough
now if you’re an org with 1k+ engineers it’s a massive cost saver
for a bank with 20k engineers spending 2-10k+/engineer/mo on LLMs, securing compute can be a massive win (also obvious privacy/security advantage)
BREAKING: Revolut moved ~2,000 engineers onto self-hosted GLM for coding and pushed Claude Code and Codex to the exception list.
Per David Abitbol, who leads AI ops there: 56 Nvidia B200s, a blended 30 cents per million tokens, roughly a tenth of what they paid Anthropic and OpenAI. Engineering lead time improved 70%.
Here's the part that matters: the gains showed up when tokens stopped being rationed. Engineers quit saving AI for the hard tickets and pointed it at everything.
Frontier models become the exception, open weights become the default. That's a pricing problem for every lab heading to IPO.
h/t @OliverMolander
Would you move your team off frontier APIs if the bill dropped 90%?
Cost is why many teams are moving to open models.
The next question is where the prompts go, and who can read them along the way. That's what attested enclaves are for.
BREAKING: Revolut moved ~2,000 engineers onto self-hosted GLM for coding and pushed Claude Code and Codex to the exception list.
Per David Abitbol, who leads AI ops there: 56 Nvidia B200s, a blended 30 cents per million tokens, roughly a tenth of what they paid Anthropic and OpenAI. Engineering lead time improved 70%.
Here's the part that matters: the gains showed up when tokens stopped being rationed. Engineers quit saving AI for the hard tickets and pointed it at everything.
Frontier models become the exception, open weights become the default. That's a pricing problem for every lab heading to IPO.
h/t @OliverMolander
Would you move your team off frontier APIs if the bill dropped 90%?