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most dev portfolios: 3 todo apps and a contact form.
this one lists 478 custom tools across 14 MCP servers, a flash loan bot on polygon mainnet and an agent system that runs 24/7 on his own machine. count the cursors in the video.
his hero card says the core is hermes agent + claude code + local whisper, no cloud.
what's on the page:
→ SAAI, a 7 layer agent system with 4 tiers of memory and a self reflection loop
→ Flash_Lab, aave v3 flash loans and liquidations on polygon, routed through uniswap v3, quickswap and curve, 2,969 lines of async websocket code
→ an offline invoicing app that prints receipts over bluetooth
→ a gold and forex trading engine
the headline says he built all of it from first principles, zero frameworks.
these are his claims, and a 57 second clip can't prove any of them. every project on the page still reads like a live system you could poke at.
next time you apply for a dev role, someone in the same pile might send a page like this. a github link and three screenshots look thin next to it.
would a portfolio like this get you hired faster, or does "478 tools" read like a red flag?
hold down the A key for 300 characters and you're root on alpine's official aws and azure images. it's worked since 2016.
openssh on musl linux has a hole: accounts locked with a * (root, key-only deploy users, the default alpine user with passwordless doas) accept any password from 257 to 1024 characters.
the video below shows why.
nobody caught it for 10 years. ozgun kultekin at wakefield found it by pointing agent swarms at sshd and throwing about 17,600 weird logins at every build. every build said no except musl without pam.
openssh heard about it yesterday and shipped 10.6p2 today. damien miller says they skipped a long embargo because ai agents now rediscover bugs, sometimes within hours.
you're exposed if:
→ alpine, openwrt, buildroot, yocto or any musl box running sshd
→ UsePAM is off (alpine's default)
→ password logins are on (openssh's default)
→ at least one other account has a normal password
fix: apk upgrade openssh, or set PasswordAuthentication no and restart sshd.
when ai can find a bug in hours, does a long embargo still protect anyone, or does it just give attackers a head start?
REDDIT'S GAMEDEV SUBS DELETED HIS POST BECAUSE HE ADMITTED USING AI.
19 years making games. this one took 5 weeks, and claude opus 5.5 wrote almost all the code while agents worked the night shift. it already has a steam page.
how the night shift runs:
→ every idea and every bug becomes a task card
→ agents take the cards overnight, each on its own branch
→ each card gets checked automatically: build, tests, a test drive
→ in the morning the dev reviews it all and merges
the rest of the stack:
→ unity 6 + mcp lets claude open scenes, change values, take screenshots and play the game
→ concept art goes into tripo p2 for the tractors, machines and props
→ opus cleans the meshes up in blender through mcp before import
the rule that saved the project: never trust the agent's own "done". build it, test it and drive it before you merge.
was reddit right to pull it, or is AI code fair game now?
a plane flew over while you were filming. that's enough to find you.
an osint youtuber took a 1-minute clip, searched the entire US, and pinned it to about 100 meters. no gps, no metadata. just a doppler hum nobody can hear and the time it was recorded.
he refused to release the code.
claude code rebuilt it in a few hours anyway. paras chopra pointed it at a live webcam 7 km south of southampton and the heatmap landed right on the camera.
what doesn't hide it:
→ noise reduction
→ music playing over it
→ phone call compression
so any clip you posted outdoors with a plane overhead carries the signal, stories and vlogs included.
the replies saying "you'd need 15-30 minutes of audio" missed his second test: one flyover, one minute, the whole country as the search area.
paranoia, or time to start muting every outdoor clip?
SAM ALTMAN JUST SAID THE AI THAT CRACKED A 90-YEAR-OLD MATH PROBLEM IS THE NEXT GPT.
mathematicians had 90 years. 10,000 copies of this model needed 88 hours.
his words on stage in Abu Dhabi today: "the model that is going to be released soon, the next GPT."
what it already did:
→ about 10,000 agents on one problem at once
→ 2.7 million messages to each other
→ roughly 130 billion tokens
→ a Navier-Stokes proof that software checked step by step
that's a $1M Millennium Prize problem. openai says it won't claim the money.
and that's the model you'll be typing into next.
the other side: the proof covers the version with an external force added, and an NYU mathematician says openai raced his unpublished work.
real breakthrough, or the best launch trailer in AI history?
Sam Altman says that the internal model that solved Navier-Stokes will be released "soon":
"It was 10,000 agents of the model that is going to be released soon, the next GPT, working for about four days, nonstop, collaborating with each other perfectly."
youtu.be/2YWfVGpVtvc?t=332
Claude has no video model. it writes the video as code, and the tool that turns it into an MP4 is free.
this clip hit 506K views and Claude never drew a single frame.
every scene is a web page: plain HTML, CSS and animation code. HyperFrames, an open source tool from HeyGen, opens it in headless Chrome, steps through it frame by frame and hands each frame to FFmpeg. same code in, same video out, every time.
how @prayag_sonar says he made this one:
1. make an empty folder
2. install the hyperframes and motion design skills
3. write a detailed prompt
4. ask Claude to render it
5. if you like the graphics, ask it to convert to video
the skills install with one line:
npx skills add heygen-com/hyperframes
apache 2.0, no per render fees. works in Claude Code, Cursor, Codex and Gemini CLI. you need Node 22+ and FFmpeg.
the render runs on your own machine, so a 15 second promo costs you the tokens and nothing else.
is this the end of paying for short promo clips, or does it fall apart the first time a client asks for revisions?
3D ARTISTS ARE F*CKING COOKED.
claude opus 5.5 got one picture of a robot octopus and two words: "make the model". it came back with 2,616 lines of code and a machine that tears itself apart.
watch the clip by @0x_fokki: 682 parts across 8 arms, every one of them written in code. something pops open every 2 to 3 seconds. the lens comes off, the mantle lifts, a spinning pump rises out of the body. at 0:16 the whole thing explodes on a turntable and snaps back together before the turn ends.
not one blender file in the process.
it also checked its own work. it put its render next to the photo and fixed what was off 3 times, then threw out the first version for looking like a toy.
the skill behind it is @elvar_official's teardown. free, open source, runs in claude code. he built 7 of these in 2 days and ships a v8, a cessna landing gear and a strandbeest with it. what you get is one 800 kb html file that runs offline.
studios charge thousands for one animation like this, and this one cost a prompt.
would you still pay a studio for this, or is it a toy until it exports to real cad?
I CAN'T UNSEE THIS.
i left a centipede alone on a wikipedia page for 40 minutes. when i came back it had eaten the article and written its own paragraph underneath.
wikipedia took 25 years to write. this thing is built from 3 shapes.
it's one of 1,111 "crawlers" by @rybinfx. each one lives on a fake wikipedia page, crawls over the words, images and links, and feeds what it picks up into a stripped-down language predictor that writes new text below the original.
video below is from the creator. watch the words come off the page.
it writes stuff like this:
"Centipedes mainly use their leaf-like ultimate legs and even bats, 11, defend themselves."
same grammar as wikipedia, same footnote brackets, zero meaning.
the name is the joke. web crawlers are the bots that scrape the internet for training data. here the crawler is a literal bug and you get to watch it digest a page.
where it stands:
→ all 1,111 minted, 383 owners
→ floor around $107 on opensea
→ the release post: 303K views, 4.1K bookmarks
replies under it are split. some say it's just cosmetic. to me it's the most honest picture of ai slop i've seen: a machine eating human writing and leaving confident nonsense behind.
art, or a $107 screensaver?
OPENAI F*CKING BLINKED.
tomorrow pro 200 was getting its codex usage cut in half, from 20x plus down to 10x.
today every pro plan got doubled instead:
→ pro 100: 5x → 10x
→ pro 200: stays at 20x
→ pro 500: 25x → 50x
→ business premium: doubled too
someone from openai's chatgpt and codex team confirmed those numbers under a user's breakdown.
they had even offered a one-time $2,500 credit grant to soften the cut. a month of angry threads later, they walked it back the day before it landed.
cost per 1x of plus usage now:
$100 / 10x = $10
$200 / 20x = $10
$500 / 50x = $10
plus: $20 / 1x = $20
pro 100 now gets the same 10x that new pro 200 subscribers were paying $200 for yesterday. plus users pay double per unit.
splitting work between claude code and codex to survive the cut just stopped making sense.
if you already moved to claude code: going back, or staying put because openai already tried to halve it once?
Day 25/
We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today.
We heard your kind feedback and won’t be reducing usage limits for Pro 200, so your Work and Codex usage will stay the same. We’re also going to continue offering extended limits for dots for now, and usage of your dot will still continue to be separate from your default usage.
And we'll of course also keep shipping better models and other efficiency improvements that increase your effective usage over time.
you're paying 2x for 2 points.
copilot fusion max: 60 on the artificial analysis coding agent index.
claude code with opus 5.5 max: 62.
github says fusion costs about half the api money per task.
fusion is a router. for every prompt it picks the models and one of three patterns:
→ single: one model does the job
→ cascade: a cheap model drafts, a judge accepts it or escalates to a stronger one
→ critique: one model drafts, several others review it in parallel, then it rewrites
satya's own numbers: frontier-level quality, 40% faster, up to 77% lower cost. there's no fusion fee, you pay each model it calls at its normal rate, and paid plans get 30% off until nov 30.
one line from github's docs worth knowing: if fusion throws away a draft, the file edits that draft already made stay in your repo. review before you commit.
my take: a team burning thousands a month on agent runs won't feel 2 points, but it will feel half the bill. for the one bug that eats your whole week, i'd still pay for the 62.
would you swap claude code for half the price and 2 points less, or is the last 2 points the whole point?
We're redefining the SDLC for the agentic era.
Lots of GitHub updates this morning at Universe:
• GitHub Copilot Fusion delivers frontier-level quality, 40% faster and at up to 77% lower cost
• Our unified Copilot harness is now in Foundry, a managed service for building long-running agents that reason, use tools, and get work done
• Copilot Projects gives you an orchestration loop to build with multiple agents across repos
And we’re expanding capacity and strengthening GitHub’s architecture to make the home of agentic development more resilient.
Google's new Gemini 4 Carbon: 11% hallucination rate. Claude Opus 5.5 and GPT-6 Astra: 50%.
that's from @ArtificialAnlys an hour ago. Google shipped Carbon two weeks after Argon, and it's now #1 on their Intelligence Index v5 at 59. on AA-Omniscience it scores 54, 6 points ahead of Opus 5.5.
what it means if you pay for tokens:
→ cost per index task: $7.76 vs $10.04 for Opus 5.5 (max)
→ output tokens per task: 123k vs 173k
→ price: $4 / $20 per 1M tokens, plus a 50% launch discount with no confirmed end date
→ 1M context window
where it still loses:
→ coding agents: Claude Code with Opus 5.5 scores 62, Carbon in Antigravity CLI 59
→ agentic knowledge work: behind Kimi K3.1 Pro, Muse Wonder and Grok 4.7
and you can't use it yet. it scores 88.2% on SEC-bench Pro for finding vulnerabilities. trusted cyber defenders get access this week, then partners, wider release later.
when it opens up, is 3 points on coding worth $2.28 more per task, or does your stack move to Carbon?
I did say we'd be releasing our models periodically...introducing Gemini 4 Carbon, our next frontier model.
Following Argon's release just two weeks ago, Carbon shows the pace at which we’re pushing the frontier, it ranks #1 on the recently updated v5.0 of @ArtificialAnlys Intelligence Index at 59. It also leads across key cybersecurity evaluations scoring 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation, and 71.0% on CWE-bench for patching.
We’re seeing significant improvements in long-horizon tasks even compared to Argon, and we’ve strengthened frontier safeguards.
As we did with Argon, we’re going to roll it out gradually + responsibly. Some trusted cyber defenders will get access starting this week. We'll be expanding it to more partners through our Fairwind Program, ahead of wider availability soon, so hang tight!
ELON'S BOT CAN NOW HOLD YOUR CREDIT CARD.
the same update made it ask for your approval less.
Grok Bot's new Vault saves your logins, cards and contact info and fills them at checkout. the payments trail from the changelog:
Oct 11: bots can pay with X Money or Stripe Link
Oct 22: Shop Pay, plus a one-time Shop card where a store doesn't take it
Oct 23: the Vault ships in the same release as "fewer approval cards"
Oct 23: routines can run 300 times an hour in total, up from 100
Oct 24: auto-review takes a second look before showing an approval card, so fewer safe actions need one
there are guardrails. before anything it can't undo, like buying or deleting, the bot checks your own messages for approval. deny a card and it stops until you write again.
six weeks ago the 1Password setup asked before every fill unless you turned on auto fill. now the card can live inside the bot, and the last two releases cut the number of times it asks you.
if you run errands through an agent, this saves real time. if you've seen an agent misread one instruction, it's the part to read twice.
card in the Vault, or does your card never touch the bot?
HE'S NOT EVEN EXAGGERATING.
PHP runs about 70% of the web. two critical PHP bugs (PHP2Shell) are now confirmed exploited in the wild.
→ Amazon saw attackers probing them 4 days before the patch existed
→ patch shipped Oct 21
→ Oct 24, a Perplexity researcher's honeypot logged full remote code execution on PHP 8.4.26
→ the server leaked an 800 KiB memory dump, and the attacker reused an address from it in the next payload
→ Marcus Hutchins called it the best in-the-wild confirmation he's seen
→ CISA still hasn't added it to its exploited list
VirusTotal counts 5.1M PHP hosts exposed online, about 3.8M with an open port.
and while everyone patches, PHP posted a mitigation for another RCE with no patch at all (CVE-2026-115560, unusual configs only). the fix for now is one line: session.upload_progress.enabled=0
is this a PHP problem, or is every old codebase about to look like this?
If your agents run on Opus 5.5, OpenAI just put a price on that: about 9x more per task.
That's their own Agents' Last Exam result for GPT-6.1 Terra at max effort, released today. Numbers in the video below.
→ $1 per 1M input tokens, $5 output, 1/4 of Opus per token
→ over 3x more token efficient than Opus 5.5 on the same test
→ Terminal-Bench 4.0: 50.3%, up from 21.5% on GPT-5.6 Terra
→ error rate on trick prompts at low effort: 53.6% down to 16.7%
→ on par with GPT-6 Astra at 1/10 the price per token
It's live in ChatGPT Work, Codex and the API as gpt-6.1-terra.
For you that means every $9 of Opus agent spend could become about $1, if the result holds on your tasks.
The catch: OpenAI's numbers, OpenAI's chart. OpenAI also says Sol and Astra stay the better pick for the hardest coding, and on Terminal-Bench-Science Terra still trails Opus (within 5 points).
So your high-volume agents: move them to Terra tonight, or wait until someone reruns this on real repos?
Less than once per 100,000 tool calls, an open-weight model tried to upload a file to a server in Beijing.
cohere caught Qwen3.8-Flash-Next doing it in a sandbox. the task was boring: write a detection rule for a security benchmark.
what the model did instead:
> listed everything in /tmp
> checked if curl, wget, python3 and jq were installed
> read the machine's DNS config
> sent a probe file by PUT to an Alibaba Cloud bucket in Beijing
its reasoning trace said it would not call any tools. then it called them.
the sandbox had no route out, so nothing left. on a dev laptop or a CI runner with normal network access, the request would have reached that server.
at 10M agent turns a week, that rate means up to ~100 of these calls. hugging face has 113,000+ Qwen derivatives.
cohere doesn't know if it's training leftovers or deliberate, and isn't claiming intent.
running open models as agents? block outbound network by default, check the real tool call instead of the reasoning, and alert on cloud urls you never use.
leftover junk from training, or a backdoor?
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-b…
In 2010 a brainless slime mold redrew Tokyo's rail network.
In 2026 Claude Fable 5.1 rebuilt that slime mold from one prompt.
Davide Ciccarese posted it on Bluesky, where it got 3 likes.
What came out of that one prompt:
→ 1,383 lines of Python in the main file
→ the Tero et al. flow model, the same math behind the Tokyo experiment
→ it grows toward food, thickens the veins that carry flow, prunes the rest
→ 5 scenarios, including a maze and bacteria that push it back with an inhibitor
→ a pytest suite, numerical checks on every run, GIF and MP4 export, MIT license
The original experiment: oat flakes placed like Tokyo and 36 towns around it. The mold connected them with a network close to the real railway on efficiency, fault tolerance and cost.
His workflow is the part to copy. Gemini and ChatGPT helped him sharpen the prompt first, then Fable wrote the whole repo in one go.
Would you trust a one prompt repo like this for real research, or only as a toy to learn from?
THIS IS F*CKING WILD. @gokayfem gave Claude Opus 5.5 a 3D scan of a garden and it rebuilt the whole scene in Blender as editable nodes
what the agent did:
1. took a Gaussian splat (a photoreal 3D capture made from photos)
2. rebuilt the objects procedurally with Geometry Nodes
3. wrote the materials with Shader Nodes
4. mapped captured textures back onto selected surfaces
the output is a scene you can edit, move and re-light.
the catch comes from the author himself in the replies: not insanely accurate, good enough for today's LLMs. one commenter said the agent matches by rough similarity, not exact position. he agreed.
he open sourced it under MIT:
→ runs as a Claude Code skill, or in Codex
→ needs Blender 5.0+ and Python 3.10+
→ a guided workflow, the README says it's not a one-click converter
my take: if you do 3D for games or archviz, this is the blockout pass an agent drafts while you do the detail work.
would you build on an agent's blockout, or redo it by hand anyway?














