me realizing ai can handle customer calls and book appointments while i still rehearse saying “hello” before calling the dentist
AVARITCALL V2 ÇIKTI, GENEL KULLANIMA AÇILDI!
işletmenin telefonu çaldığında müşterinin ne istediğini öğrenen, bilgilerini toplayan ve randevu akışını yürütebilen bir ai asistanını artık chatgpt’ye tek bir prompt girerek kurabiliyorsunuz.
ister panelden, ister api veya mcp üzerinden. asistanın görevinden konuşma tonuna kadar ayarlayabiliyorsunuz.
dahili chatbot, crm, her personel için ayrı takvim, görev takibi ve notion’dan bitrix24’e kadar hazır entegrasyonlar da var.
bir oto servis üzerinden kurulumunu, kullanımını ve maliyetini açalım.
the mispricing around sonnet 5.5 isn't its benchmark score.
it's what a cheaper, faster execution layer does to distribution.
anthropic reports 30%+ faster output and up to 30% lower cost for most work. use it for bounded, repeatable jobs. reserve opus for ambiguity, architecture, and final judgment.
the saved cost can buy more iterations, shorter wait times, or lower prices. that changes who can ship a useful loop at scale.
the advantage won't belong to the team with the best model spreadsheet.
it'll belong to the team that converts inference savings into product velocity, margin, or reach.
i think cats in Türkiye do this exercise.
geçen ay tiktokta bu arkadaşın bu challengeına denk geldim, kullanıcı adı: taylar holliday
gördükten bir gün sonra hemen bu challenge’a başladım, 1.5-2 ay önceki fiziğimden eser yok şimdi, kollar dahs geniş, six-packsler daha belirgin. herkese tavsiye ediyorum. en azından deneyin, sağlık her şeyimiz…
the market keeps treating distribution as the last step after the product works.
for agent businesses, it is part of the product.
every handoff creates a chance to learn what users ask for, where they abandon the flow, which exceptions need a human, and what should become a default action.
the company that owns that loop can improve acquisition and automation at the same time.
the mispriced asset is not reach.
it is the feedback path between demand and execution.
the agent demo proves the model can act.
retention is decided by what happens after the action: retries, approvals, state, exceptions, and the handoff back to a human...
that layer is boring plumbing, so it gets less attention than the model. but it is where a one-off capability becomes a daily habit, and where the vendor learns what the customer actually values.
the market is pricing autonomy as the product. the durable advantage is recovery.
whoever owns the recovery loop owns the cost, the trust, and eventually the workflow.
the hackers told 404 media this wasn’t financially motivated. they described their plans as coercion. that makes the alleged family information especially concerning.
404 media examined a sample appearing to contain 5,000 employee records and corroborated some phone/name matches. that doesn’t independently establish that every employee and applicant was exposed. the FBI hadn’t responded to the outlet when the article was published.
if authentic, the records could support fake recruitment messages to applicants, impersonation targeting spouses, or harassment outside work. those are potential consequences, not incidents confirmed with this dataset.
for anyone building products that collect sensitive data, there’s a practical question here: does the business actually need every personal detail it stores?
an emergency contact field might look harmless inside an HR dashboard. in a stolen export, it could expose someone who never had an account.
getting the portal back online can’t be the entire recovery plan.
claude opus 5.5 is interesting for a reason that has almost nothing to do with another benchmark win.
anthropic cut pricing to $4/m input + $20/m output. GPT-6 Astra is $10/m + $50/m. on raw token pricing, Astra is 2.5x more expensive.
that gap barely matters for one prompt. it matters a lot once the product is an agent making dozens of calls across research, coding, retries, tool use and long-running workflows.
this is where model economics start turning into product economics.
lower cost per loop means you can run more attempts, keep margins healthier, or price the end product more aggressively without changing the business model.
the next frontier fight isn’t just who has the smartest model. it’s who can deliver enough intelligence per dollar that entire categories of agent products suddenly make economic sense.
your business eventually becomes a visible version of your internal operating system.
every pricing decision, hire, delay, risk, compromise and standard compounds from what you believe about money, yourself and what you deserve.
people think they’re building a company.
most of the time, they’re building a mirror.
mispriced retweeted
The next big advantage in AI won’t come from writing better prompts.
It will come from building better systems around the models.
Think about AI coding agents. Instead of repeatedly asking them to write cleaner code, you can define project-specific rules, give them access to the right tools, provide examples of acceptable output, and automatically test their work.
The difference is significant: you’re no longer relying on a single instruction to produce the right result every time.
You’re creating an environment where the model has the context, constraints, and feedback it needs to work more reliably.
The real skill is shifting from telling AI what to do to designing systems that help it consistently do the right thing.
prompt engineering is starting to feel like legacy terminology.
the model isn’t really being steered by one prompt anymore. in a good ai system, behavior gets distributed across layers:
> rules
> skills
> tools
> examples
> schemas
> evals
the prompt is just one control surface.
instead of telling a model to write good code, you encode repo rules, define when tools should be used, show examples of acceptable output, constrain responses with schemas, then run evals to see if the whole thing actually works.
that’s the shift: less wordsmithing, more environment design.
the gap between good and bad ai users probably won’t come down to who writes the cleverest prompt. it’ll come down to who builds the better environment around the model.
prompt engineering is shrinking.
system design is becoming the actual skill.
mispriced retweeted
the bigger consequence is that company knowledge has to become executable.
today a lot of important judgment still lives in people's heads:
- when to escalate.
- what counts as risky.
- which exceptions are acceptable.
- how quality gets checked.
- which tools are allowed.
if agents are going to do real work, that knowledge can't stay tribal.
it has to get encoded into rules, tools, schemas, evals and approval boundaries.
prompt engineering was mostly about communicating with a model.
the next skill is turning how a business operates into an environment a model can reliably act inside.
prompt engineering is starting to feel like legacy terminology.
the model isn’t really being steered by one prompt anymore. in a good ai system, behavior gets distributed across layers:
> rules
> skills
> tools
> examples
> schemas
> evals
the prompt is just one control surface.
instead of telling a model to write good code, you encode repo rules, define when tools should be used, show examples of acceptable output, constrain responses with schemas, then run evals to see if the whole thing actually works.
that’s the shift: less wordsmithing, more environment design.
the gap between good and bad ai users probably won’t come down to who writes the cleverest prompt. it’ll come down to who builds the better environment around the model.
prompt engineering is shrinking.
system design is becoming the actual skill.
prompt engineering is starting to feel like legacy terminology.
the model isn’t really being steered by one prompt anymore. in a good ai system, behavior gets distributed across layers:
> rules
> skills
> tools
> examples
> schemas
> evals
the prompt is just one control surface.
instead of telling a model to write good code, you encode repo rules, define when tools should be used, show examples of acceptable output, constrain responses with schemas, then run evals to see if the whole thing actually works.
that’s the shift: less wordsmithing, more environment design.
the gap between good and bad ai users probably won’t come down to who writes the cleverest prompt. it’ll come down to who builds the better environment around the model.
prompt engineering is shrinking.
system design is becoming the actual skill.
if your ai support agent uses a frontier model to answer "where is my invoice?", you're paying for a specialist to read a label.
- classify the request
- retrieve the account context
- draft the routine reply
- escalate ambiguity
the mispriced asset isn't intelligence. it's the boundary that decides when intelligence is worth buying. the operator who owns it captures the margin.
the more interesting consequence is economic.
once tiny decisions become cheap enough, you stop asking one expensive model to "run the agent."
you start building a hierarchy of intelligence.
cheap models handle reflexes.
frontier models handle uncertainty.
code handles authority.
that's when agents start looking less like chatbots with tools and more like actual software systems.
i think people are misunderstanding what jev is.
it's not another model that's supposed to replace claude code or codex.
it's more interesting as the decision layer sitting next to them.
claude/codex are good at the expensive stuff:
understanding a repo
planning
writing code
debugging
reasoning through weird edge cases
but real agents also make hundreds of tiny decisions:
should i retry?
which tool should run?
is this action risky?
did the task actually finish?
does this require human approval?
which model should handle the next step?
using a frontier reasoning model for every one of those decisions is overkill.
jev is designed for this layer.
you give it some state and ask structured questions.
it can return a choice, a score, or basically a probability that something is true.
so a coding agent could look more like:
claude code / codex
> understand + execute the task
jev
> route, classify, score risk, decide retry/escalation
deterministic code
> enforce permissions and hard rules
human
> handle high-risk or low-confidence cases
example:
codex changes authentication code.
jev checks:
"does this touch a sensitive system?"
"how risky is this diff?"
"should a human review it?"
high confidence + low risk > keep going
uncertain > send it back to claude/codex for deeper reasoning
high risk > stop and ask a human
same thing works for browser agents.
you don't need a giant model reasoning for 10 seconds before every click.
let the fast model handle the reflexes and wake up the expensive model when actual reasoning is required.
that's the part i find interesting.
the future agent stack probably isn't one giant model doing everything.
it's multiple intelligence layers:
fast decisions.
slow reasoning.
deterministic rules.
human escalation.
aka less "one ai agent"
and more an actual cognitive architecture.
token pricing makes inference read as a commodity. the hidden bill is the serving shape: long prompts, output length, cache misses, queueing, retries, and the hardware sitting idle between bursts. two teams can pay the same per-token rate and get radically different gross margins. the leverage moves to whoever designs the request path, not whoever memorizes the model menu. a cheaper input matters only when utilization, latency, and recovery remain inside the customer’s promised boundary.
the mispriced part of ai influencer ugc isn't the avatar. it's the acquisition loop around it.
cleo's public tiktok case used creator-led creative with smart+ and reported 38% lower cost per incremental subscription, 66% more registrations, and 71% more in-app payments. liner's case reported a 155% lift in influencer-driven subscriptions. the actual loop is problem-led creative, tracked conversion, paywall iteration, then paid scale. ugc becomes mrr infrastructure only when it feeds that loop.
source boundary: ads.tiktok.com/business/en-U… and airbridge.io/en/case-studies… report campaign lifts, not audited proof that ugc alone caused mrr growth.
tiktok tarafında bizim kafamız artık çok net: önce reklam çekmiyoruz, önce içerik formatını çözüyoruz. ürünü alıp ilk saniyede kameraya sokmak yerine ugc mantığında gidiyoruz. videonun başında tek derdimiz adamın yaşadığı problemi kendisine göstermek. "bu ürün çok iyi" demiyoruz, "senin başına gelen şey bu" dedirtiyoruz. ürün ancak o problem kafada oturduktan sonra sahneye giriyor. o yüzden çoğu videoda ilk birkaç saniye tamamen hook + problem, ürün tarafı daha sonra geliyor.
ai influencer'ı da burada kullanıyoruz. ama sıfırdan güzel bir yüz üretip her videoda ürün sattırmak gibi düşünmeyin. karakterin yüzü, odası, telefonu tutuşu, konuşma şekli, hatta videolarındaki küçük kusurlar bile mümkün olduğunca aynı kalıyor. yani hesapta bir gün sarışın kız, ertesi gün başka bir render dolaşmıyor. insanın takip ettiği şey ürün kataloğu değil, tanıdığı bir karakter oluyor. görüntüyü de özellikle fazla sinematik yapmıyoruz. hafif kötü kadraj, gerçek telefon hissi, cilt dokusu, normal ev ışığı... ai'nın "fazla güzel" yaptığı ne varsa geri bozuyoruz.
sonra asıl iş başlıyor. tek videoya aşık olmuyoruz. aynı probleme 10 farklı yerden giriyoruz: birinde direkt acı noktası, birinde yanlış yapılan şey, birinde demo, birinde yorumdan gelen itiraz, birinde "bunu kimse böyle kullanmıyor" açısı. hangisi yürürse onun metnini kopyalamıyoruz, yapısını söküyoruz. hook neydi, insan neden kaldı, ürün kaçıncı saniyede girdi, hangi cümlede yorum geldi, nerede paylaşıldı... çalışan şey buysa ertesi gün aynı yapının 5 farklı varyasyonunu çıkarıyoruz.
ilk saatlerde de görüntülenmeye bakıp sevinmiyoruz. videonun ilk 6-18 saatinde asıl baktığımız şey insanlar kaldı mı, tamamladı mı, kaydetti mi, dm'den birine attı mı, profiline geçti mi. 100 bin izlenip kimse ürünü merak etmiyorsa bizim için güzel video, kötü marketing. 12 bin izlenip profil ve ürün sayfasına insan taşıyorsa esas winner bazen o oluyor.
ücretli marketing'i de ilk viral videoyu görünce açmıyoruz. aynı açının ikinci kez çalışmasını görmek istiyoruz. çünkü bir video şans olabilir, iki ayrı creative aynı problemi vurup benzer davranış üretiyorsa artık elimizde bir yapı var. o noktadan sonra çalışan videoyu spark ads'e taşıyıp daha fazla dağıtım satın almak mantıklı hale geliyor. zayıf creative'e para döküp kurtarmaya çalışmıyoruz, organikte kendini kanıtlayanı büyütüyoruz.
bizim tiktok oyunu aşağı yukarı bu kadar aslında: ugc ile problemi bul, ai influencer ile ölçeklenebilir bir yüz kur, organikte açıyı test et, winner'ın yapısını çoğalt, sonra parayı dağıtıma koy. çoğu kişi önce 30 video üretip sonra neden satmadığını düşünüyor. biz önce insanın neden durduğunu çözüyoruz, video üretimini ondan sonra hızlandırıyoruz.
the bigger move here isn't the feature merge. it's resumable state.
claude is making chats, projects, artifacts, connectors, and skills persist across the handoff, then turning the same context into docs, decks, and designs. that shifts value from answering a prompt to owning the work after the laptop closes. claude captures the workflow context; standalone ai wrappers lose distribution leverage. the real test is whether users can return to the same state and finish faster without rebuilding it.
the expensive model is often being asked to do the cheapest thinking. route classification, extraction, and routine formatting to smaller models. reserve expensive inference for ambiguity, escalation, and final judgment. the mispriced asset is not intelligence alone. it is the router that decides when intelligence is worth buying. the decision boundary sits in workload fit: the path that turns a theoretical resource into a paid, repeatable outcome.
meta's strongest safety argument may be its most dangerous assumption: that giving everyone a personal superintelligence automatically creates a balance of power.
it doesn't, if the agents are personal only at the interface. if the same few companies control the weights, compute, identity layer, app distribution, memory storage, and update pipeline, then millions of people can hold a personal agent while the control plane remains centralized. that isn't a balance of power. it's centralized power wearing a personalized skin.
worse, distribution can multiply the attack surface. one bad update, one compromised permission system, or one silent policy change can propagate across millions of agents at once.
the real safety test isn't how many people have access. it's whether users can inspect, fork, switch, audit, and shut down the system without asking the provider for permission. if they can't, personal superintelligence is still rented power.
Last month I wrote about how we can build a positive and safe future for everyone: meta.com/thefutureisforevery…
Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens.
The reality is:
- People won't want to use agents that are misaligned with them and that don't do what they ask, so labs have a strong natural incentive to make their models more aligned.
There is a lot of debate about slowing progress on capabilities until alignment catches up. My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn't focus on alignment will fall behind.
- Labs face significant liability if their models cause harm, so they have a strong incentive to prevent this as well.
Meta delayed shipping Muse for several months to focus on safety and security. We didn't call for everyone else to do this before we would. We just did it as part of our day-to-day work because it was clearly the right thing for people and for us. I'm proud of the security foundations we've built.
- Engaging independent evaluators and advisors is industry best practice. MSL already does this today in several areas because it helps produce better work. Other labs can just do this too. In general, it would be helpful for there to be a larger and more diverse ecosystem of evaluators.
- Committing the significant majority of compute towards serving people rather than racing towards recursive self-improvement is one of the best ways to ensure we develop this technology safely. Meta has made this commitment and other labs can do this as well.
I believe the key to building a positive future for everyone is maintaining the right balance of power. This is within our power to do.
the important distinction here is that $1.2T would be the valuation, not $1.2T sitting in a bank account.
but if a raise at that level is even close, the endpoints get ridiculous.
they could lock up years of chip supply, power, data-center capacity, and top-tier talent before smaller labs can compete. they could subsidize inference until AI stops being a separate product and becomes an invisible layer inside every app, company, and device. they could fund agents that don't just answer questions, but own entire workflows from research to execution.
the biggest bet isn't a better chatbot. it's buying the cost curve, distribution, and daily habit loop early enough that everyone else has to build on top of it.
valuation isn't proof of execution. it just tells you how much future the market is being asked to price in.
