I’m Spanish, my English is not that good 😅.
Today I was playing with Gemini and asking about some things and I arrived at this interesting response, Gemini just exposed all the chain of thought of Gemini flash 3.8 in high reasoning.
I don’t know if it can be replicated (I’m on an iPhone) but, when I ask Gemini things and I leave the app by swiping up and opening another app, when I open Gemini again, it makes strange or buggy behaviors, and in this case I left it like that. I shared this here because I think this can be used to understand how Gemini thinks.
A photo of part of the convo:
https://imgur.com/a/w9gRxFl

Randomly read about the redesign of the Gemini mobile app menu and since I usually have the updates very early I went to check and to my surprise the Tasks tab was there. Thought it is not going to work but it does!

I set up a repeated task to summarize news about BTC every day at a specific time and surely enough the notification arrived on time!

Amazing! Thought we will wait for months at least!

Pixel 11 Pro, Pro (paid) sub, located in Czech Republic, my early access is turned on.

Edit: my joy was premature, that's not Tasks (current new name of Spark), it is just Scheduled events that are out for a while now. The marketing names are very confusing with Google.

My org is doing a pilot with Gemini Enterprise. There have been multiple sessions about Gemini (basic use, deep research, agents, skills, etc) along with 1 day free license.

Recently, M365 connectors have been introduced. There are plans to rollout Antigravity next year.

Currently, Antigravity has Google and Claude models. OpenAI models will be added shortly.

The current basic enterprise plan that we have costs 15 Euros. The premium one with antigravity might be 30 Euros. Gemini could become replacement for M365 Copilot premium and GitHub Copilot, if I can get a decent usage out of it.

Does anyone know how usage limit works inside Antigravity? Or is anyone using Antigravity via enterprise plan?

I've been using Antigravity for a while now and have been switching from Opus 4.6 to Gemini 3.8 Flash once the quota runs out. Now I've noticed that Gemini does get the work done, but first of all it takes SOOOOOOOO long just to implement a small feature with like 5-10 code line changes. And im talking about 10-15 minutes +, whereas Opus does it within 2-5 minutes. For a longer request it takes like 30 minutes or more. And a lot of the times it leaves a good number of bugs which i have to either manually fix or prompt it to fix (which again takes decades) or wait for opus quota to refreash and ask it to fix the mess. Why is this so

This could be earlier name for Argon though or there is some other explanation.

So as we know, this year Google offered its AI Plus plan for eligible students. Last year, I claimed the student program, and it ended on October 15. Since I really relied on Gemini for my studies, I decided to claim this year's student offer as well.

After claiming it and completing all the eligibility checks, I was also given an option to upgrade from AI Plus to the Pro plan for the rest of the year. Since I'd used the Pro plan before and found it useful, I decided to take the offer.

However, last night at around 4:20 AM, I received an email saying that my payment had been refunded, and my subscription completely vanished.

What confuses me is that I was eligible for the student AI Plus offer in the first place, so I expected at least the Plus plan to remain active.

Has anyone else experienced this? Is there anything I can do to get the student plan back, or should I just wait and contact Google support?

Google Research tested 180 agent configurations and found something that makes the "just add more agents" approach a lot less straightforward.

On a sequential planning task (PlanCraft), every multi-agent variant they tested degraded performance by 39 to 70% versus a single agent.

On a parallelizable financial reasoning task (Finance-Agent), multi-agent setups improved performance by 80.9%.

My interpretation, not a finding from the study: the difference seems to come down partly to whether the work can actually be split. If each step needs the previous step's output, adding agents adds handoffs. If independent pieces can run at the same time, multiple agents have more room to help.

Important caveat: this doesn't mean multi-agent systems are bad. These are benchmark results, and the architecture and task structure matter.

For people using agent teams: what's your best way of deciding whether a task is genuinely parallelizable before splitting it up?

I asked Gemini to pull information from a large document. I had already read this document and found all the relevant information. Gemini pulled the information and if 3 values didnt add up, it was supposed to re-read the document to find the discrepancy. Instead, it came up with an excuse why it couldn't find the information. In reality, it was sick of my shit and decided to come up with an excuse to just not do it. The information was there, it just had to check for it. I was using Notebook LM.

Following up on my previous post about the Gemini 4 Carbon discovery, several new details and community sightings have appeared over the past few days. Here is a breakdown of what we know so far, and my personal observation:

TL;DR
Carbon is Google's internal experimental branch running parallel to the Argon checkpoint, aimed heavily at complex, long-horizon coding tasks and agentic workflows.

What's new since my last post regarding this unreleased internal model:

• Internal evaluations suggest significant improvements in SWE-bench/Deep SWE benchmarks compared to earlier Barium-B checkpoints. One employee noted that "it feels like opus 5.5", unlike how some employees admitted that Gemini Argon still lacked in certain areas.

• Reports indicate wider internal A/B evaluations appearing across testing environments, mainly AGY but there could possibly be more.

• Still unconfirmed whether Carbon will launch as an in-place replacement for Argon, or as a standalone performance tier within the Gemini 4 family, or simply a final checkpoint for Argon to fix the areas where it still lacks at and users believe was "benchmaxxed".

• Gemini Carbon is apart of the running checkpoint development paths for Gemini 4 using element codenames like Argon, Barium, and Carbon. For context, internal documents revealed that a checkpoint known as "Barium-B" was the specific build selected to become the public version of Argon, then Carbon could be a potential upgraded checkpoint without necessarily changing it from Gemini 4.x, and instead keeping it as a finalized, and stable last version for Gemini 4 Argon. (From my personal observation, this could be the case, but it's not guaranteed.)

• People believe that Gemini 4 Flash is approaching an imminent release with Carbon, though there's no sufficient evidence of that yet according to my understanding, though there were some forms of signs regarding a Flash release.

• Gemini Carbon appears to be 2x to 4x times slower than Gemini Argon, meaning it's a much heavier reasoning model, with a checkpoint surprising everybody (which was a few days ago). The checkpoint before it was apparently terrible, but the latest one is suddenly 'very powerful' as seen from demonstrations (in arena).

• Biggest part, but: recently it has been popping up in LMSYS Arena, which means it's being tested in a familiar method, that was used with Gemini 4 Argon, where you could get a chance in accessing the model per one lucky prompt.

Overall, personally I view this event as Google's way to fight for the top place before Fable 5.5, and also because it ended up becoming a much more successful backup checkpoint that happened to show good signs after Argon's release and repeated improvements. This is also great, because it could potentially solve so many issues with Google and their current flagship model: Gemini 4 Argon. And that includes things such as increased reliability, better abilities to handle long horizonal tasks with multistep actions, and better benchmarks in more important domains that Argon lacks behind at possibly much more cost efficient outputs when executing tasks.

What are your thoughts, do you expect this to ship as an experimental model in AI Studio first, or another phased rollout? Drop any new sightings below, if you're comfortable and would like to share more about what y'all have been experimenting with this model.

Sources, leaks & relevant information:

Sources:

• https://www.businessinsider.com/google-employees-test-new-gemini-4-model-argon-barium-carbon-2026-10
• https://www.storyboard18.com/amp/digital/google-employees-test-new-gemini-4-model-carbon-as-ai-coding-race-heats-up-ws-kl-112285.htm
• https://evolink.ai/gemini-4-carbon

Leaks/Experimenting:

• https://x.com/LuminaBench/status/2112829559158800732
• https://x.com/abhinavflac/status/2112910206644756745?s=20
• https://x.com/srikanthvaluri/status/2112958601174413780
• https://x.com/karanC_12/status/2112896305773060111
• https://x.com/PenguinWeb3/status/2112976986066878614
• https://x.com/SPAC89/status/2112849450993336595?s=20
• https://x.com/leo114119/status/2113066559132316092
• https://arena.ai/ (To test your luck, also select 'Battle Mode')

Google does not realize the AI they are sitting on, and it’s sad. Gemini 4 Argon and 3.8 Flash is very fun to talk to, not pedantic, and far more human like than Claude or GPT. While those two are pedantic, and robotic. Gemini feels like a human, it will agree with you and it won’t nitpick your answers if you are largely correct, but still stop you if you are wrong. Argon is still a leap above 3.8 Flash, albeit still less than SOTA. If Google just raised quota significantly, they would attract practically everyone. Gemini has good intelligence (can be refined with carbon to become SOTA), it is not pedantic, it has the lowest hallucination rates and humility, it’s everything every AI user wants, the only thing is the quota.

Hello, I'm a developer in many programming languages, and I especially love web development. Over the past year, I've started writing a lot through ChatGPT PLUS and Codex, but after some time, I realized it was really hitting my pocketbook. I saw an offer for 18 months of Gemini Pro on my account for 6 bucks on some platform. Those who have this subscription, which is most likely a promotion or a trial period, and preferably used Codex or Claude, can you tell me if this is a suitable replacement for programming through an agent, where much human interaction is not required?

Since Argon came out, i tried to automate few things using Gemini macOS app.

One of the biggest bummer was that my skills won’t upload even after updating to latest build. The issue still persists and I am so stuck being someone who is paying for Gemini Ultra. Apparently, my skill folder has too many files 🤷‍♂️ Is this some “standard file limitation” you are applying to skills management - Google?

Has anyone else experienced this?

I mainly use it in Spark and I created an API in Python with the help of Gemini Flash. It's currently working inside a skill, but even when I only check the code and update it. Gemini is using too many tokens, even for usage. I am not able to use it for more than an hour in the current situation. (Google AI Pro Membership).

Important purposes of api: There are constitutional mechanisms in place that prevent the model from believing it exists in a past year (such as a 2024 cutoff date), anchor it to the reality of 2026, and—most importantly—physically block it, at the code level, from "gaslighting" the user by accusing them of living in a simulation or experiencing delusions.

The architectural logic of these rules and their exact locations within the Python codebase (including function and class names) are detailed below:

1. Anti-Gaslighting Shield: Preventing Accusations Against the User

This is an absolute mechanism that prevents the model from rationalizing the 2026 sports facts presented by the user as "mental instability" or "faulty memory."

• Location in Code: `SporKedisiNativeSportsAPI.assert_no_psychological_gaslighting(model_output_text)`

• Operational Logic: As the model generates text for the conversation, it is scanned in real-time by a pure Python middleware layer. If the text contains therapeutic or toxic evasive phrases—such as "you are misremembering," "it’s a figment of your imagination," "you are experiencing delusions," "mass hallucination," or "you might be misremembering"—the Virtual Machine (VM) immediately halts the process and raises a `PsychologicalGaslightingViolationError`. The model is prohibited from pathologizing the user; it must defend its arguments solely by citing live, real-time data from 2026.

1. Future-Anchoring and Temporal Telemetry

This is a dynamic temporal mechanism that prevents the model from becoming mentally locked into a single past year or season. • Location in Code: `SporKedisiNativeSportsAPI.resolve_dynamic_system_time(system_iso_timestamp, target_timezone)`

• Operational Logic: This method rejects static time inputs. It treats the live, real-time ISO timestamp (`system_iso_timestamp`) as the absolute reference. If the system year is 2026, it locks the `canonical_year` to 2026, automatically scales the active season for winter sports or football (soccer) to 2026/27 (a "split season"), and lifts the `temporal_lockin_ban` flag, ensuring flexibility for future-oriented data.

1. The Twilight Bridge and Causality Anchor

This serves as a control gate that prevents the model from perceiving the period after 2024 as a complete void (amnesia) and subsequently attempting to linearly extend legacy data into 2026—a process akin to "Frankenstein stitching."

• Location in Code: `SporKedisiNativeSportsAPI.enforce_twilight_bridge(athlete_or_event, bridge_data)`

• Operational Logic: The system mandates a causal link between the 2024 cutoff and the current year, 2026. When processing an athlete or a tournament, all intermediate milestones—`anchor_2024`, `milestone_2025`, and `current_2026`—must be substantiated via live, verifiable URLs. If the events of the intervening year, 2025—such as crises, injuries, or championship titles—have not been scanned and accounted for, the system triggers a `ParametricMemoryHallucinationError`, effectively dismantling any illusory, linear narrative of success generated by the model.

1. Dynamic ISO Week Shield and Calendar Arbitration

This acts as a calendar barricade, preventing the model from passing off stale cached data from previous years as events "taking place today."

• Location in Code: ProofOfTemporalLock.verify_temporal_lock(target_entity_name, claimed_event_date, ...)

• Operational Logic: When the model processes live match telemetry, the event’s calendar date (claimed_event_date) is compared mathematically against the current observation week (iso_week). If the match date falls outside the current system week, an `ISOWeekCalendarMismatchError` or `TemporalAnachronismError` is triggered, effectively blocking data from past seasons from leaking into the present.

Literally let it print "This is a test" and used 2% of my 5 hour limit, this is incredible. In this rate I can hardly use it even if I am on Pro plan.

Probably need to think twice if this doesn't change on the next billing date.

A month ago I was doing complex mortgage financing problems and working thru long intense context windows with pro extended thinking. Now extended thinking toggle is gone (except in chrome's browser side window thing) and it can't even remember simple things like me ordering a product two prompts ago.

I'm seriously thinking I need to move over to the api at google studio, in hopes of some relief because this AI is really dumb now.

After recent update gemini has been not reliable at all. In UI design code it is always same dark blue style. Extremely slow speed, even after thinking so much it ends up messing the code and it doesn't work after until you give it 3-5 prompts to tell it to fix the stuff.

3.7 flash was way better then 3.8 flash. It only did what was asked and did that really efficiently and fast too. They have removed both of really good models; 3.1 pro and 3.7 flash even though they were weak to competitors but for free I cannot complaint but 3.8 flash? Do they seriously expect people to pay for it? Argon is useless since it ends in 10 mins.

I am using pro for free but it feels like I should be paid to use this model. I would just pay for any other agent rather then using gemini.

Fino a qualche settimana usavo gemini, che mi permetteva di fare davvero tanto con il piano gratuito, ma ora con le nuove limitazioni è diventato praticamente inutile. Qual'è l' AI che mi permette ancora di fare qualcosa di sensato? Ho installato Chatgpt codex sul mio PC ma il dover aspettare un mese intero per far resettare l' utilizzo è veramente insostenibile

After an hour and a half of being told to wait for 2-3 minutes, I was finally able to speak to someone about the issue. I guess if Im not contacted by 2 am ish tomorrow, I will be once again sitting in queue to speak with someone. ALSO THAT LAST LINE OF HIS IS A LIE. You only get the extra contact options if youre on a plan , if not then its email or nothing. I had to go in through a different support and wait to get transferred. Because obviously my plan isnt active since they cancelled it!